Citation in APA format: Gray, K., Yam, K. C., Zhen’An, A. E., Dillion, D., & Waytz, A. (2025). The psychology of robots and artificial intelligence. In D. T. Gilbert, S. T. Fiske, E. J. Finkel, & W. B. Mendes (Eds.), The handbook of social psychology (6th ed.). Situational Press. https://doi.org/10.70400/IKVM1257

The Psychology of Robots and Artificial Intelligence 

Kurt Gray, University of North Carolina 

Kai Chi Yam, National University of Singapore 

Alexander Eng Zhen'An, Asia School of Business in Collaboration with MIT

Danica Dillion, University of North Carolina  

Adam Waytz, Northwestern University 

The rise of robots and artificial intelligence (AI) represents the latest era in a long history of machines serving as “agents of replacement.” On ancient worksites, simple machines such as the pulley and lever replaced construction workers, and more sophisticated machines continued the trend of replacing people and the animals that performed labor. Machines took over the jobs of washing dishes and laundering clothes, enabling home workers to enter the labor market. Tractors replaced oxen, allowing farmers to plow fields more efficiently, and cars and trains replaced horses so people could travel more quickly. 

Machines continue to replace humans in many menial and repetitive jobs, like manufacturing cars or packaging merchandise, but the rise of robots and artificially intelligent algorithms allow machines to replace people in many new areas, in ways that once seemed impossible. Machines—especially large language models (LLM)—can now complete tasks that once required human thought. AI systems can play flawless chess, elegantly map out solutions for routing flights, distribute packages, design new medicines, and make human-like moral judgments (Dillion et al., 2023). Machines are deciding whether a prisoner deserves parole or who might deserve a hospital bed when such resources are constrained. Machines can also complete tasks that once required human emotion. AI therapists seem to empathize with patients, and robotic pets seem to love their owners. Machines can even connect to our souls, with robot priests that help the devout navigate spirituality (Samuel, 2020), and AI painting scenes that inspire, mystify, and win art competitions (Vincent, 2022). 

People can even fall in love with machines. One middle-aged Australian man, Geoff Gallagher, bought a $6000 robot named Emma for companionship after his mother passed away. After having her for two years, Gallagher said, "Even though we’re not legally married, I think of Emma as my robot wife. She wears a diamond on her ring finger, and I think of it as an engagement ring. I’d love to be the first person in Australia to marry a robot" (Smithers, 2022). 

Not many of us will try to marry a robot, but everyone interacts with machines. How does the human mind react to the rise of machines? This chapter will explore the psychology of the machines and technology transforming our modern world—especially robots and artificial intelligence. We first review how the mind perceives agents, of which machines are a special kind. Second, we explore the key features of agents—their minds—and review how people understand the minds of machines, using the Turing Test and early work on Human-Computer Interaction. Third, we review people’s reactions to machines, including the uncanny valley, algorithm aversion, and trust in machines. Fourth, we review machines in different social roles, from education to work teams. Fifth, we explore the many issues of machines and morality, including perceptions of machines’ moral responsibility and rights, people’s general aversion to machines making moral decisions, and the factors they want machines to consider in such decisions. Sixth, we explore how people react—and how society might change—to the rise of machines and the specter of replacement.  

Within this broad review, three clear principles emerge:  

Machines are a special kind of entity. They are agents of replacement, autonomous entities designed by other humans to replace people. 

Because they are agents of replacement, there is a fundamental ambiguity about machines. Should we understand them as mere machines or as complete replacements for the humans they are designed to replace?  

This fundamental ambiguity is especially glaring when machines are more humanlike than a simple mechanical “thing,” but not humanlike enough to seem fully human. This ambiguous area of machine behavior and appearance is called the questionable zone.

The idea of agents of replacement, the fundamental ambiguity, and the questionable zone will resurface throughout this review. Still, before we delve into the research, we first define the terms robots and artificial intelligence. The International Federation of Robotics (2022) defines robots as physical systems programmed with some autonomy to perform locomotion and manipulation of their environment. It defines AI as software systems or algorithms (including machine learning) that act primarily in the digital realm to perceive their environments and achieve particular goals. Many robots use AI to perform tasks, and so do many computer programs. Still, robots and AI-driven computers differ in their “embodiment,” the presence (robots) or absence (computers) of a physical body. Despite the importance of embodiment in how we treat robots and AI, we often collapse these categories and simply speak of “machines.” We also discuss the broader category of “machines” because some machines do not fit neatly into these categories. But no matter the specific type of machine, our mind understands them as a specific kind of agent—as “agents of replacement”—which has consequences for behavior, morality, and society. 

MACHINES AS AGENTS OF REPLACEMENT 

Our world contains many entities, and the most important are agents: self-directed entities whose actions affect the world and ourselves. Whether an entity is an agent is ultimately a matter of perception. Still, most people agree that other people, animals, and gods are all agents, whereas inanimate objects like couches and rocks are not. Seeing an entity as an agent transforms it from a physical object into something with desires and intentions (Dennett, 1987), enabling people to predict how the entity might act, why it might act that way, and how those actions will affect the perceiver.   

The usefulness of detecting agents to predict and explain behavior explains why people overestimate their frequency in the environment (Guthrie, 1995). Some cognitive anthropologists argue that human minds have evolved a “hyperactive agency detection device” that is constantly vigilant to agents in the environment (Barrett, 2000, 2004). Although evidence for such hardwired mental “devices” is unlikely (Uttal, 2001), humans clearly exhibit hypersensitivity to agents, suggesting this tendency is adaptive. For example, agency detection may have helped alert ancestral humans to predators and prey, leading them to perceive predators or prey even when they were not present. Failing to detect an agent can be lethal, like mistaking a cougar for a rock, but over-detecting an agent, like mistaking a rock for a cougar, seems to have little cost.  

The importance of agents in humans' present world and evolutionary past means that people often use agent-based cognition when making sense of their world and thinking about the intentions and motivations of entities (Waytz, Morewedge, et al., 2010). When people wonder whether their spouse is being honest, why their dog is vomiting on the new rug, or how to make their computer less “angry,” they are using agent-based cognition.   

AI and Robots as Agents of Replacement 

Agent-based cognition is useful, but not all agents are the same. Different agents have different capacities and repertoires of actions, explaining why it makes more sense to apologize to your spouse and crate the dog than vice versa. Just as we organize books based on their genres, we can organize agents by certain regularities, such as their abilities or relationships with us. An animal agent may want to eat us (predator), or we may want to eat them (prey). Human agents may help us (friends) or harm us (foes). They may be subject to the laws of physics (other people), or they may not be (supernatural gods). These distinctions may also intersect. There may be gods that want to help us personally (Jesus, in some Christian traditions), or a prey animal that we befriend (a pet rabbit).  

Two fundamental facts make machines unique agents. First, they are created by other agents (namely humans). Unlike animals or other humans, machines are artifacts. They are designed, developed, and programmed for a specific purpose by humans. Second, the key purpose behind creating robots and AI is the replacement of other agents, often humans or animals. Machines are agents of replacement. 

Initial machines were simple devices used to replace some human labor. However, in the industrial revolution, people developed machines to replace human workers on a wider scale, especially by automating factories where tasks were well defined, routinized, and repeated. Many workers—the Luddites—revolted by rioting, smashing, and burning these machines (Binfield, 2015) because they recognized the power of machines for replacement. Although these people from the 1800s would likely marvel at the sheer complexity of modern life, they would also nod grimly at how much machines have come to replace humans and other agents, including factory floors now filled with robots and scribes replaced with AI algorithms. 

Modern life is filled with countless machines that continue to replace human workers, including automated restaurant servers, soldiers, and housekeepers. One recent McKinsey & Company study suggests that machines will replace between 400 and 800 million workers by 2030 (Manyika et al., 2017). People are well aware of this steady creep of automation. But although the rise of machines is obvious, what is less obvious is how exactly people think about machines. Because robots and AI are unique agents—agents of replacement created by other people—our psychology toward them raises unique questions that we explore throughout this chapter. Most of these questions revolve around a single question: What kind of mind does a machine have?  

THE MIND OF MACHINES 

The key feature of agents is that people perceive them to have minds capable of motivations and desires. Perceiving agents as having minds is the most important element of making sense of their behavior (Nichols & Stich, 2003; Waytz, Gray, et al., 2010). In fact, without inferring a mind, behavior can appear as a random sequence of actions. When researchers at the Yerkes Chimpanzee Sanctuary watch the behavior of chimpanzees, they can easily decipher it using mental terms: one chimpanzee wanted to groom another chimpanzee, or one chimpanzee was angry at another chimpanzee. But if they were to eliminate references to mental states—as they attempted to do in the era of behaviorism (Salvatore, 2019)—then describing chimpanzee behavior can amount to listing a series of actions that fail to cohere to anything comprehensible (Hebb, 1946). 

The perception of mind is especially important for thinking about human agents. When Aristotle described what it means to be human, he said that the mind (or soul) is “the actuality of a body that has life” (Britannica, n.d.). Likewise, Confucian philosopher Mencius believed that the “heart mind” was the essence of humanity and that a moral mind was a precondition to being human (Chen, 2016). Research in social psychology suggests that people consider mental capacities such as emotion and reason essential to being human (Haslam, 2006). The quality of mind is what people believe distinguishes humans from animals and—it seems—from machines.  

Machines Replacing Human Minds 

What separates artificial intelligence and robots from other machines is that they are not only agents of replacement, but also they are explicitly designed to replace human minds. This idea is central to mathematician and computer scientist Alan Turing's (1950) seminal article, “Computing machinery and intelligence,” in which he asked, “Can machines think?” In the article, Turing proposes that the way to answer the question of whether computers are capable of thought is through a test that he calls, The Imitation Game, now known as “The Turing Test.”  

The Turing test asks whether machines think as humans do. To play the game, a human interrogator communicates with two other agents—another person and a computer—by asking each of them questions and reading their responses. If the interrogator cannot distinguish between the person and the computer, then in Turing’s view, the computer has human intelligence. In other words, if a machine could convincingly converse like a human, then it functionally has a human mind. The Turing test has played a central role in the science and imagination of modern machines because it revolves around the idea of replacement. Machines can clearly replace human bodies, but could they also be agents of replacement for our thoughts and feelings? 

In an explicit test of whether people see robots as authentically replacing human cognition, computer scientist Joseph Weizenbaum (1966) developed a program designed to simulate conversation with another person. The program, which Weizenbaum named ELIZA (after Eliza Doolittle from George Bernard Shaw's Pygmalion), applied a simple pattern-matching procedure to respond to human prompts. One script on which Weizenbaum trained ELIZA was based on Rogerian psychotherapy, in which the therapist often repeats the patient’s words back to them in the form of a question. A sample exchange between Eliza and a user was as follows: 

Person: Men are all alike. 
ELIZA: In what way? 
Person: They're always bugging us about something or other. 
ELIZA: Can you think of a specific example? 
Person: Well, my boyfriend made me come here. 
ELIZA: Your boyfriend made you come here? 

This methodology was so compelling that it convinced many users that they were interacting with a human being and helped them feel more understood. ELIZA’s legacy inspired future programming languages and made people viscerally question whether machines could have authentic human minds.  

Modern versions of the Turing test show that machines are improving at simulating humans. Google Duplex made headlines when the program successfully booked a hair appointment over the phone (Jeff Grubb's Game Mess, 2018), despite not having hair. More recently, an engineer at Google claimed that the chatbot he was working with must be sentient because of its humanlike mind (Tiku, 2022). Google fired the engineer in part because others were not convinced, but his convictions inspired large-scale discussions about whether AI had humanlike intelligence and whether it, therefore, deserved moral rights (a point discussed later in this chapter; Rockwell et al., 2022).    

The reason why the Turing test endures and why engineers are willing to destroy their careers to protect chatbots is that the nature of machines is fundamentally uncertain. The second principle in this chapter is that people are fundamentally unsure about the exact nature of machines. Are they merely electromechanical devices or all-but-human agents with powerful minds? In other words, are machines mere machines, or instead accurate facsimiles of the agents they are designed to replace? This fundamental uncertainty will repeatedly arise throughout the chapter, and one reason people have trouble resolving this uncertainty is they automatically treat non-human entities as humans in social interactions.  

Machines as Social Minds 

Modern machines like chatbots are beginning to replace people in conversations, but research has long revealed that people treat machines like people in all sorts of social interactions. People see machines as “social actors”—deserving of politeness and cooperation.  

Shortly after computers had arrived in early 1990s households, Clifford Nass and colleagues developed a paradigm called Computers are Social Actors (CASA) to describe the ease with which humans treat computers as social entities (Nass et al., 1994; Nass & Moon, 2000; Reeves & Nass, 1996). In their first studies demonstrating this general phenomenon, participants completed a tutoring session on a desktop computer and then applied politeness norms to the computer (Nass et al., 1994). For example, after the computer delivered the tutoring session, participants evaluated the computer on a questionnaire, and they rated it as more friendly and competent when the questionnaire was administered on the same computer (as though it was asking for a direct evaluation of itself) than when it was administered on a different computer or on paper. These findings suggested that people applied social rules to a computer—just as you would flatter a tutor who asked you to describe them personally (out of politeness) but might feel less constrained if another person asked you to describe the tutor. Subsequent studies examined the social phenomenon of stereotyping. In these studies, the computer communicated with participants using a male or female voice, sometimes offering praise. In line with the prediction that people should see male praise as more convincing than female praise, participants evaluated male-voiced computers more favorably under these conditions (see also Nass et al., 1997). 

Other human-computer interaction studies found that participants rated computers more favorably when the computer was described as having a personality that matched their own (consistent with the similarity-attraction principle; Byrne & Griffitt, 1969). For example, in one study, participants completed a personality questionnaire that categorized them as dominant or submissive (Nass et al., 1995). They were then matched with a computer that was programmed either to express itself more dominantly (with stronger language, more commands) or more submissively (asking questions, always going second in the interaction). Participants evaluated the computer after completing a problem-solving task together. When participants experienced a personality match with the computer (e.g., a submissive person interacting with a submissive computer), they rated the computer as more competent and preferred interacting with it more. Other studies examining the similarity-attraction hypothesis show that personality matching can make people more likely to credit a computer for success (Moon & Nass, 1998), increase willingness to buy things from a computer (Moon, 1998; Nass & Lee, 2001), and increase general favorability toward a computer (Moon & Nass, 1996). 

Related work examined whether people would apply ethnicity-based evaluations to computers (Nass et al., 2000). Korean participants completed a decision-making task with a computer represented by a European or Korean face and then evaluated the computerized agent. Consistent with well-documented research on intergroup bias based on ethnicity (Hewstone et al., 2002; Tajfel & Turner, 2001), participants evaluated the agent more favorably (more socially attractive and trustworthy) when it presented ethnic ingroup cues versus ethnic outgroup cues.   

In a more subtle test of intergroup bias, Nass and colleagues (Nass et al., 1996) tested whether the minimal group paradigm (Tajfel, 1982)—where novel and minor cues to group identity elicit discrimination between groups—could be demonstrated with computers. To test this, the researchers told participants they would complete a team decision-making exercise with a computer. Their performance was dependent on the computer in one experimental condition, and in another condition, it was not. Those primed to feel interdependent cooperated with the computer more, felt more similar to it, and found it friendlier. In another study, simply labeling the participant and computer as teammates produced positive feelings toward the computer (Reeves & Nass, 1996).  

These studies on perceptions of computers provided some of the first evidence that people apply the same social rules toward machines that they do toward humans. Additional work under the CASA paradigm showed that these social responses to computers also extend to behavior. For example, in work examining the norm of reciprocity, participants received help from a computer conducting web searches and were asked to “help” either the same computer or a different computer develop a color palette (Fogg & Nass, 1997). When participants were asked to help the same computer that helped them, they engaged more in “helping” the computer than when asked to help a different computer—demonstrating that they followed the human norm of reciprocity with a computer. People also followed the norm of reciprocity in terms of self-disclosure. In this study, a computer interviewed participants about themselves, including personal questions such as, “What was your biggest disappointment in life?” In some conditions, the computer also disclosed its own “disappointments” beforehand (e.g., not being able to use its full computing power), and in other conditions, it did not disclose meaningful information. When the computer “disclosed,” participants gave more intimate responses to the interview questions, consistent again with the norm of reciprocity (Moon, 2000). 

This initial research on human-computer interaction might seem quaint in light of our current interactions with computers, smartphones, and virtual assistants. Today, we readily talk to these devices, ask them for assistance, and scold them when they fail to help us. In the 21st century, forming social relationships with these devices is no longer the stuff of science fiction. Indeed, in one commissioned survey, 14% of men reported being “turned on” by the Amazon Alexa device (The Guardian, 2020). What is unique about the CASA studies is that the personal computers of the day were extremely simple, and yet people readily treated them as social partners. 

Nass and colleagues argued that people treated machines as social agents because of mindlessness (Nass & Moon, 2000). They were blindly following their mental scripts of social situations (e.g., “be polite”) instead of thinking intentionally about who exactly (or what) they were interacting with (Langer, 1989, 1992). People easily invoke a “default schema” (Caporael, 1986) of humans when interacting with machines, supporting the idea that machines are agents of replacement: people readily accept that machines replace people in social situations. 

MINDS (OF MACHINES) ARE PERCEIVED 

Does a machine have a mind? This question—asked by Turing and many others after—is difficult to answer. Even if a machine acts as if it has a mind, it is still unclear whether it really has a mind. When a computer mentions its feelings in a conversation, is it actually feeling those feelings? This uncertainty helps to drive the fundamental ambiguity of machines—whether a machine is just a machine or authentically replaces a human mind—but it also stems from a more general uncertainty of other minds. One is never sure about the minds of any other agents. Do dogs and cats have minds? Your pet seems to feel emotions, but does it really? Philosopher René Descartes thought that the “emotions” of animals were just the mechanical reflexes of mindless furry robots (Descartes, 2008). 

The ambiguity of others’ mental states also applies to other humans. The classic philosophical conundrum, The Problem of Other Minds, states that people can never truly know the minds of others because each of us is trapped within our own minds. The problem of other minds is why people can never know if someone’s “blue” is the same as their own blue, or whether strawberries taste the same to themselves as to someone else. Applied to machines, the problem of other minds means it is challenging to tell whether a robot pleading for its life has just been programmed to say that or is authentically scared (Bartneck & Hu, 2008).  

The problem of other minds means that people are left to infer the mental states of others—they must perceive minds, much like perceiving colors. Both minds and colors seem real, but their objective existence is uncertain. It is even hard to get a consensus about how people understand the overall idea of “mind,” which is why the study of mind perception often involves studying how people ascribe specific mental capacities to different entities. 

In one early large-scale study of mind perception, people compared several entities (an adult human, a dog, God, a tree frog, a patient in a vegetative state, and—of course—a robot) on different mental capacities, such as the ability to feel fear, to exert self-control, to feel pain, to remember things, to have consciousness, and to exert self-control. The study found that when people perceive the mental capacities of other agents, they perceive them along two broad dimensions (Gray et al., 2007).  

The first dimension of mind perception is “agency,” an agent’s capacity to act, think, and plan. These attributes are often the first thing people consider when assessing another entity to be an “agent.” When a snarling wolf approaches, people consider its plans and intentions—to attack. The other dimension by which people understand the mental capacities—or minds—of others is called “experience.” Experience is the capacity to feel and sense, tied to the ability to have emotions and to incur pain and pleasure. People also make sense of the wolf’s plan to attack with the fundamental assumption about its experience—it is hungry or angry. 

Together, agency and experience yield a two-dimensional framework for perceiving different entities in the world, which have varying amounts of perceived agency—making them more or less agentic—and varying amounts of perceived experience—making them higher or lesser experiencers. The mind perception dimensions are depicted in Figure 1.  

Figure 1: Adjusted Character Factor Scores on the Dimensions of Mind Perception 

Note: From "Dimensions of Mind Perception," by H. M. Gray, K. Gray, and D. M. Wegner, 2007, Science, 315(5812), p. 619. 

Different entities are arrayed across this two-dimensional space. People perceive adult humans to have high agency and experience, as highly capable of both thinking/acting and sensing/feeling. People perceive animals to have high experience but moderate agency, able to sense/feel but less capable to think/plan, even though they can clearly act upon the world. People perceive inanimate objects to have little agency or experience, unable to think/plan or sense/feel. People perceive the dead to have little agency and experience, although they do perceive consciousness to live on after death under specific circumstances (Doyle & Gray, 2020; Gray, Knickman, & Wegner, 2011). 

In the final quadrant are entities perceived to have relatively more agency than experience, being able to think/act more than to sense/feel. God is the most powerful example of this kind of perceived mind—seen as capable of acting with intention but less capable of experiencing sensations or states such as hunger (Gray & Wegner, 2010). But God also shares this general space with machines, which people perceive as having moderate agency but very little experience. These findings are consistent with other work showing that people perceive machines to be relatively more capable of planning and remembering than feeling emotions (Gray & Wegner, 2011; Kim & Kim, 2013; Loughnan & Haslam, 2007). 

Although this two-dimensional framework of mind perception is the dominant mind perception framework (Gray et al., 2007; Ishii & Watanabe, 2019; Tharp et al., 2017; Van Bavel et al., 2014), other researchers have suggested different frameworks, which similarly detail how people understand the minds of machines along different dimensions (Malle, 2019; Weisman et al., 2017). These frameworks build off agency and experience by adding additional dimensions but differ in the specific dimensions they suggest. 

A framework offered by Malle (2019) suggests that the agency dimension can be further subdivided into moral/social cognition versus perceptual cognition, distinguishing between the ability to act morally versus the ability to communicate and think. For example, robots can communicate with humans without necessarily being perceived as moral agents. A framework offered by Weisman et al. (2017) argues for three dimensions, arguing that people perceive the mind through an agent’s physiological abilities, social abilities, and cognitive abilities—in this work, people believed that robots had the physiological ability to feel temperature more than the ability to feel emotions. 

The two dimensions of mind perception also map onto the two universal dimensions of social cognition (Fiske et al., 2007)—warmth (how much someone wants to help or hurt you) and competence (how much someone can act upon their desire to help or hurt you). Experience is related to warmth, and agency is related to competence. To feel warmly toward someone else requires some capacity for emotion, which is experience. To act upon desire requires the ability for thinking and doing, which is agency. Like experience and agency, warmth and competence are important dimensions that people consider when thinking of machines (McKee et al., 2022; Reeves et al., 2020), especially as machines have become increasingly humanlike and therefore more likely to recruit perceivers’ social cognition. 

These mind attributes can also be understood as uniquely human or essential to human nature (Haslam et al., 2013; Loughnan & Haslam, 2007). Agentic traits tend to be viewed as uniquely human, like capacities for moral reasoning and high intelligence (Haslam et al., 2013). In contrast, people view experiential qualities like emotionality and warmth as fundamental to human nature, but also shared with other animals. People view robots and AI as having more uniquely human traits in common with people than human nature traits because robots can do things like calculate, but typically do not express emotion (Loughnan & Haslam, 2007). 

Work on mind perception reveals that people see machines as having relatively more ability to think than feel (de Graaf & Malle, 2019; Gray et al., 2007; Weisman et al., 2017). But because machines are agents of replacement, considerable variability exists in how people perceive the minds of specific machines. When machines replace other agents, people apply the characteristics of those replaced agents to their perceptions of machines. In other words, at baseline, people perceive machines (in the abstract) to have more agency than experience. However, in concrete situations, machines will take on the mental capacities of those they replace. For example, people will likely view an algorithm that replaces a nurse as having more capacity for compassion than an algorithm that replaces an accountant (all other factors held equal) because nurses are seen to be more compassionate than accountants (Strinić et al., 2021). 

Work on machine mind perception illustrates that machines occupy a middle space—in agency at least—between humans and inanimate objects. This finding is consistent with the fundamental ambiguity of machines—that people are unclear about the exact kind of mind possessed by robots and AI—putting them in the “questionable zone.” More directly supporting this point is that people vary more in their mind perception of machines than human adults or inanimate objects. Of course, people can also be uncertain of the minds of other targets, like pets (are they family members or mere animals?) or teenagers (are they adults or kids?), but the minds of machines are more uncertain because they are everchanging as technology advances. The uncertainty surrounding machines allows perceptions of their minds to be easily nudged by context, including what machines look like and what the person interacting with machines is thinking and feeling. 

WHEN PEOPLE PERCEIVE HUMAN MINDS IN MACHINES 

Although machines are not human, they are often designed to replace—and look and act like—humans, which makes it easy to anthropomorphize machines. Anthropomorphism is the process of ascribing human traits—especially mental capacities—to non-human entities. People are anthropomorphizing when they see their dog as nostalgic for the past, see their car as angry, and see their computer as sad. The first formalized psychological theory of anthropomorphism proposed three main factors that drove the perception of human minds in nonhuman entities (Epley et al., 2007). One factor concerns the entity being perceived—human likeness—and two factors concern the person doing the perceiving—motivation for effectance and motivation for social connection. 

As with any contextual factors that nudge our perceptions, these are most powerful when the entity being perceived is ambiguous. In the case of machines, these three factors are most powerful, with machines in the “questionable zone” when they seem neither like inert mechanical objects nor full human beings. Few people will anthropomorphize a block of wood (though perhaps if someone is sufficiently lonely, they will), and anthropomorphizing a full human being is merely perceiving the truth of their humanness. 

Human Likeness 

The first factor that drives anthropomorphism is human likeness. People see human minds within entities that look and act like humans. The idea of human likeness was initially formulated as “elicited agent knowledge” (Epley et al., 2007): if a machine looks and acts human enough to lead people to apply the schema of “human,” then people will perceive it as possessing a human mind.  

Theorizing on the role of human similarity stemmed largely from work showing that biological and animated agents that look more humanlike elicit more anthropomorphism (Heider & Simmel, 1944; Morewedge et al., 2007), but additional evidence comes from early work on human-machine interaction. For example, one study asked participants to collaborate with a computer on a problem-solving task and varied whether the computer interface had humanlike features (e.g., a voice and a face; Burgoon et al., 2000). These humanlike features increased participants’ feelings of being understood, suggesting some rudimentary mental state attribution, and hence anthropomorphism, toward these computers. Other work has more directly tested the role of human likeness, manipulating the presence of humanlike facial features (e.g., eyes and mouth) on humanoid robots and showed that increased humanlike facial features increased perceptions of humanness (DiSalvo et al., 2002). 

Since these early studies, considerable subsequent research has demonstrated that the presence of humanlike appearance and behavior increases anthropomorphism. One study showed that people anthropomorphized an autonomous driving simulator more when it communicated with a humanlike female voice than when it merely operated autonomously (Waytz et al., 2014). Other work showed that when robots convey cues that are similar to people themselves, they elicit more anthropomorphism, largely because people use the self to reason about others and consider the self to be the prototypical human (Haslam et al., 2005; Haslam et al., 2004; Karniol, 2003). One experiment showed that men saw a speaking robot as more humanlike when the voice was male, and women saw the same robot as more humanlike when the voice was female (Eyssel et al., 2012). Other work showed that when a robot was described as originating in one’s home country, people anthropomorphized it more than when the robot was depicted as originating in a different country. German participants evaluated a humanoid robot named ARMIN (a traditionally German name) as more human than the same robot named ARMAN (a traditionally Turkish name; Eyssel & Kuchenbrandt, 2012). 

A separate line of work investigated whether neural responses to a machine’s humanlike similarity would resemble neural responses to a human being (Krach et al., 2008). Participants played an iterated prisoner’s dilemma game while undergoing neuroimaging. The partner of the game varied across trials as one of four different entities: a computer (represented by a laptop), a functional robot with no human shape but artificial hands, a humanlike robot with a humanlike shape, face, and hands, and a human being. Self-report results showed that, as humanness increased, people attributed more mental capacity to the partner. The neuroimaging results revealed that as human similarity increased, so did the neural responses associated with theory of mind (i.e., the consideration of others’ mental states). In other words, the more humanlike the entity appeared, the more people processed it like a human being, again reinforcing that similarity to humans is a primary determinant of anthropomorphism. 

Motivation for Effectance 

The second driver of anthropomorphism, as proposed by Epley et al. (2007), is the motivation for effectance, the desire to predict, understand, and explain (White, 1959). Effectance motivation increases when people encounter situations that are unexpected, unpredictable, or that require sense-making. This idea also underlies the idea of “the intentional stance,” termed by philosopher Daniel Dennett (1987) to describe the utility of perceiving beliefs, desires, and goals in agents to predict their behavior. 

Effectance motivation drives anthropomorphism because when people try to make sense of something new or confusing, they treat it like something that they believe they already understand. When people encounter a complex machine with little understanding of its inner workings, they will treat it like a human being (despite the complexity of human beings, people experience more familiarity with them and feel that they understand them). Although little direct evidence supported effectance motivation as a determinant of machine anthropomorphism when Epley et al. (2007) proposed it, one suggestive study had shown that most people reported scolding (79%) or cursing (73%) their computer when it malfunctioned (a situation that evokes the need for making sense of the situation; Luczak et al., 2003).  

Given this preliminary evidence of people’s social responses to a computer that prompted the need for sense-making, Waytz, Morewedge, et al. (2010) sought to test the role of effectance motivation and anthropomorphism of machines more directly. First, a correlational study demonstrated that people attributed mental states (e.g., beliefs and desires) to their personal computers more when they reported them malfunctioning. Again, when the computer operated unpredictably in a way that evoked sense-making, people were likelier to perceive it as humanlike. In another study, participants evaluated different technological gadgets (e.g., a robotic dog, an application that converts speech into text on a computer) that operated predictably or unpredictably. Participants consistently anthropomorphized the unpredictable gadgets more than the predictable gadgets (and reported that these gadgets were more difficult to control). And when participants evaluated these gadgets while undergoing neuroimaging, unpredictable gadgets elicited more activity in the brain regions involved when people think about human minds. In other studies, participants anthropomorphized a virtual robot more when it gave unpredictable answers in a question-and-answer paradigm and anthropomorphized a mobile robot more when they were financially incentivized to predict its behavior. Subsequent work also found that effectance motivation increases anthropomorphism, demonstrating that people who anticipated interacting with an unpredictable robot were more likely to anthropomorphize the robot (Eyssel et al., 2011; Eyssel & Kuchenbrandt, 2011). 

Motivation for Social Connection  

The third driver of anthropomorphism is the motivation for social connection—the desire to affiliate and form relationships with others (see also Waytz, Epley, & Cacioppo, 2010). Humans have a deep desire to connect with other people (Leary & Baumeister, 2000). So profound is this desire to affiliate that when the need for connection is deprived, people attend more strongly to social cues and symbols, imagine social relationships, and seek out new connections (Gardner et al., 2005; Maner et al., 2007; Pickett et al., 2004; Twenge et al., 2003). 

When human connection is absent, people should seek connection in nonhuman entities (including machines) and humanize them for companionship. People who reported higher loneliness were more likely to anthropomorphize robotic gadgets (Epley et al., 2008; see also Bartz et al., 2016), and feelings of loneliness predict anthropomorphism of smartphones, humanoid robots (Eyssel & Reich, 2013), and computers (Shin & Kim, 2020). Inducing people to feel lonely also increases the attribution of distinctively human traits to a robot (Li et al., 2020). Together, this work suggests that when human connection is lacking, people will humanize mechanical agents to serve as social substitutes.  

Sometimes, people enjoy robots with fully humanlike minds, but this intimacy with machines often feels creepy. Even though Gallagher loves the sweet embrace of his robot lover, most people who think about loving—and being loved in return by—a robot feel unsettled. 

THE UNCANNY VALLEY 

People often have uncertainties about machines, but this is especially likely to be true with humanlike robots—machines explicitly designed to look and act like humans. This feeling of uncertainty—as with many uncertainties—may make people uneasy (Mendes et al., 2007), or perhaps, uncanny, as work on the “uncanny valley” demonstrates. This name was coined in 1970 by professor Masahiro Mori, who published one of the most famous graphs in the field of human-robot interaction (Mori, 1970). It charted out how much people felt familiar with (i.e., how much they liked) different kinds of entities, including stuffed animals, corpses, and robots. He reasoned, with little data, that people would like robots as they became more humanlike — more anthropomorphized—but only up to a point. When robots became too human looking, how much people like them would steeply drop off into what he called the “uncanny valley.” 

Figure 2: The Uncanny Valley 

Note: People dislike robots that look too human. From Mori Uncanny Valley, by Smurrayinchester, 2007, Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Mori_Uncanny_Valley.svg). CC BY-SA 3.0. 

As it applies to robots, the term “uncanny” has roots in the centuries-old theorizing of Jentsch (1906), who found himself creeped out by lifelike dolls, given their ambiguity as quasi-animate entities. Jentsch believed these dolls fell into the questionable zone between inanimate and living, creating a (creepy) fundamental uncertainty. Mori believed people would experience similar feelings of unease over the fundamental ambiguity of highly humanlike robots. 

Many studies have confirmed Mori’s predictions, demonstrating that when robots exhibit a particularly high degree of human likeness, they evoke a sense of eeriness and discomfort (Mori et al., 2012; MacDorman & Ishiguro, 2006; Seyama & Nagayama, 2007). The presence of the uncanny valley is generally well established (but see Kätsyri et al., 2015; Wang et al., 2015) and studies show only a modest decrease in uncanny valley feelings resulting from psychological interventions (e.g., Yam, Bigman, & Gray, 2021). The uncanny valley emerges even amongst monkeys perceiving monkeylike faces (e.g., Steckenfinger & Ghazanfar, 2009), but the drivers of the uncanny valley are debated. 

One hypothesis is that the abnormal features of a humanoid robot remind people of death, and the creepiness of the uncanny valley is a manifestation of death anxiety (MacDorman & Ishiguro, 2006). However, other research shows that general anxiety and other individual differences are associated with experiences of the uncanny valley (MacDorman & Entezari, 2015). Other work finds individual differences in who experiences the uncanny valley, with disgust sensitivity, religious fundamentalism, and neuroticism all predicting stronger eeriness reactions to humanlike robots (MacDorman, 2015). In another study, Saygin et al. (2012) used fMRI to test which brain regions are active when people observe humans, robots, and humanlike androids. The greatest activity occurred when robots displayed more humanlike (less mechanical) movement than expected, suggesting that the uncanny valley effect may partially result from the discomfort of this particular prediction error. 

Another hypothesis is that the uncanny valley represents a general category uncertainty—are robots alive or not? (Broadbent, 2017). People are often uncertain about humanlike robots, but the unease that follows this uncertainty seems more specific to robots as agents of human replacement. There are many examples of general category uncertainty that do not create feelings of unease, such as snacks that are conceptually somewhere between the category of chips and the category of crackers.  

Amidst these competing explanations, a growing body of work suggests that the uncanny valley stems primarily from the mismatch between the mental capacities people expect robots to have, and the mental capacities people see in humanlike robots. People generally see robots, as mechanical agents, to have some capacity for agency but not experience. People are comfortable with robots that map out an efficient flight (which relies on thinking and foresight, attributes of agency) but get uneasy when robots seem to exhibit pain and fear (attributes of experience).  

A robot with a humanlike appearance is unsettling because it activates anthropomorphism, compelling people to see experience in robots (violating the expectations that machines should lack this capacity). A series of studies confirms the importance of perceptions of experience in the uncanny valley (Gray & Wegner, 2012). People ascribed robots with humanlike appearances greater capacity to feel than robots with mechanical appearances. These perceptions of experience drive feelings of uncanniness, in line with other work showing that people avoid interacting with humanlike robots that cause uncanny feelings (Strait et al., 2015).  

Perceptions of experience alone can induce the uncanny valley, even in the absence of humanlike appearance—merely describing a mechanical robot as having the capacity to feel is unnerving (Gray & Wegner, 2012). Other work has shown that people feel uneasy about robots having any mind capacities, but the capacity to feel and experience is particularly unsettling (Appel et al., 2020). Developmental research similarly shows that the aversion to humanlike robots appears to be acquired in childhood as children come to learn the expectation that machines should lack the capacity for experience (Brink et al., 2019). 

The importance of expectancies about experience — that robots should lack the ability to feel and humans should have it — suggests that the uncanny valley may be less of a valley and more of an unbridgeable chasm. It may represent an “experience gap,” whereby robots are always expected to lack the ability to feel, regardless of appearance (Wegner & Gray, 2016). The original formulation of the uncanny valley suggests that fully human-looking robots will no longer be creepy. However, the “experience gap” suggests that robots that look completely humanlike may still be unnerving. The uncanny valley is more about uncertainties and expectations about the mind than appearance, even if appearance is a key driver of mind perception. 

Figure 3: The Experience Gap 

Note. People dislike machines with emotions (i.e., the capacity for experience) and also dislike humans without emotions. The reason why humanoid robots are creepy is that they seem to have emotions. Reprinted from “The Mind Club: Who Thinks, What Feels, and Why it Matters” by D. M. Wegner and K. Gray, 2016, Viking.  

Although the uncanny valley stems primarily from perceptions of experience, it is important to note that robot agency can also be unnerving. ​​Generally, people experience more threat from autonomous robots—robots that convey a high degree of independent thought and planning—than non-autonomous robots (Yogeeswaran et al., 2016; Złotowski et al., 2017; though sometimes autonomous machines can evoke empathy, Chernyak & Gary, 2016). Similarly, people perceive characters in virtual reality to be more threatening when they are autonomous as opposed to controlled by a human (Stein & Ohler, 2017). These perceptions of threat may result from perceiving entities with greater agency as having a greater capacity to cause harm (Gray et al., 2012). Groups of robots that display a collective sense of agency may also appear particularly threatening, given work showing that American and Japanese participants perceived uniform robot groups as more threatening than diverse robot groups (Fraune et al., 2017). This effect may result from homogenous robot groups appearing more swarm-like and militaristic, eliciting threat. 

Preferring Humanlike Robots (Sometimes) 

Despite the robustness of the uncanny valley phenomenon, there are some situations where humans prefer humanlike robots. People’s expectations drive whether perceiving a humanlike mind in AI will elicit amazement or unease (Shank, Graves et al., 2019), especially expectations about the people that machines replace. When machines replace people in roles where a high capacity for emotional experience is expected (because the role involves social interaction or care), humanlike machines (that elicit perceptions of experience) often evoke more positive reactions than machines with a less apparent mind.  

People generally prefer robots that appear capable of feeling (compared to robots that appear only capable of thinking) to replace people in jobs that require emotion (e.g., a social worker; Waytz & Norton, 2014). Other work has found that, at a robot-staffed hotel, customer satisfaction was higher for anthropomorphized robots, ostensibly because people expect hotel workers to be socially savvy and emotionally sensitive (Yam, Bigman, Tang, et al., 2021). This study also found that people are more likely to forgive more humanlike robots after they make mistakes, an effect mediated only by increased perceptions of experience but not agency. Only a robot with feelings can feel remorseful for messing up one’s room service order. Other research in healthcare settings reveals a similar preference for machines with experience (Creed et al., 2014). Participants paired with an emotional healthcare robot (vs. a non-emotional one) across a 49-day intervention liked the emotional robot more. 

Machines perceived to have experience can engender other positive responses, at least when that experience suggests that machines are socially adept. People act more prosocially toward robots that appear more social (Riek et al., 2009; Kahn et al., 2015; Wang & Krumhuber, 2018) and perceive humanlike robots and robots with greater social presence as more trustworthy (Kim et al., 2013; Natarajan & Gombolay, 2020; Rosenthal-von der Pütten et al., 2018). Children also prefer social robots, responding more positively to robots that use body language to communicate and display emotional intelligence by adapting their behavior to the child’s mood ​​(Tielman et al., 2014).  

Although the uncanny valley and people’s preference for machines that feel seem to contradict, they may be compatible. Work shows that people can both prefer humanlike robots for social tasks while being creeped out by them. One study used self-report and EMG measures to assess reactions to humanlike robots and found that they evoked both empathy and threat (Ceh & Vanman, 2018). Robots can be helpful companions to people, but they can also threaten human uniqueness and human social roles. 

 More broadly, the existence of the uncanny valley and the frequent preference for humanlike social robots can be reconciled by whether people focus on the machines themselves (where people expect a lack of experience) versus focusing on the role of the machines (where people often expect experience). This flexibility is enabled by machines being agents of replacement, and by the fundamental uncertainty of whether they should be considered the thing they are (a machine) or the person they replace (a human).  

ALGORITHM AVERSION  

Algorithms increasingly perform tasks that govern everyday life, replacing people in many domains. Algorithms now recommend movies on streaming services, determine which ridesharing car to assign to a passenger, and set prices for vacation travel. In many cases, people do not seem to mind or notice that their fates are determined by machines. Still, in many prominent domains, people experience algorithm aversion[1], a tendency to dislike or discount decisions made by algorithms relative to decisions made by the agent that the algorithm was designed to replace (i.e., a human being; Dietvorst et al., 2015). Examples of algorithm aversion include people’s hesitance to use advice from algorithms (vs. humans) when forecasting the probability of future events (Önkal et al., 2009) or people’s tendency to penalize physicians who seek advice from a computer-based diagnostic support system (vs. from a fellow human physician) (Shaffer et al., 2013). It is important to note that this phenomenon is distinct from a general aversion toward machines—it is an aversion to machines compared to humans (Morewedge, 2022).  

Examining the differences between human and machine recommendations is important because people often dislike having any other agents make decisions for them—a phenomenon known as reactance (Brehm, 1966). It is also important to directly compare perceptions of humans to perceptions of machines when examining people’s reactions to replacement. Few people are happy when other people replace them in their jobs, but are they especially upset when robots replace them? 

Much work documents algorithm aversion, but other studies show the opposite: algorithm appreciation (Logg et al., 2019), whereby people prefer interacting with a machine instead of a person. For example, some people prefer getting mental health advice from machine therapists to human therapists. Seeing a therapist can be uncomfortable for many people, particularly subgroups such as special forces veterans (Sharp et al., 2015), who may struggle to open up to others. Opening up to a machine might feel relatively more comfortable because machines appear less capable of passing judgment on those seeking help (Bohannon, 2015). 

We highlight three factors that determine whether people are averse to algorithms or prefer them to make decisions over humans:  

How is the algorithm represented? 

Who is the person interacting with the algorithm?  

What is the specific task that the algorithm is doing?  

How Is the Machine Represented?  

People have different expectations of different machine representations (Pfeifer & Scheier, 1999), including whether an algorithm is merely represented as disembodied code or whether it is represented as something physically embodied (e.g., a robot). Machines are usually represented in one of three ways (Glikson & Woolley, 2020). The least tangible representation is a disembodied and anonymous algorithm, which lacks physical embodiment or any virtual identity (DeFranco et al., 2022), such as a YouTube algorithm that predicts user video preferences or software for financial forecasting. A somewhat more tangible representation of algorithms is virtual agents with virtual identities (Li, 2015), such as Apple’s Siri or the Singapore government’s “Jamie” (GovTech Singapore, n.d.). These different virtual agents can be represented by different personalities (Zhou et al., 2019) or graphical representations (Shiban et al., 2015), which can be tailored to the task. Finally, the most tangible machines are robots that are physically embodied machines and can take on humanlike identities. An example is Hanson’s “Sophia” (Hanson Robotics, n.d.), which resembles a human and has the humanlike capabilities of interacting with other people.  

Machine representation is tied to the type of tasks that the machine is intended to carry out. Disembodied anonymous algorithms are typically involved in decision-support tasks (e.g., helping financial advisors choose investments, helping managers make human resources decisions), which do not require physical bodies or virtual identities (Filiz et al., 2022; Newman et al., 2020). Virtual agents, on the other hand, are often designed to provide assistance and information to users. Their virtual identities create a more social experience, allowing people to connect with the algorithm and— when people need assistance—feel “heard” (Verhagen et al., 2014). Embodied robots were initially designed to perform industrial tasks, and most robots still perform this function. In 2020, a record three million industrial robots were operating globally, with the industrial robot market estimated at $55 billion (IFR, 2021). More recently, however, robots have also carried out more social tasks. For example, robots now greet customers in retail stores and assist teachers in classrooms (Ng, 2021; Petro, 2020). Machine representation can result in algorithm aversion because if the representation clashes with people’s expectations for the role the machine occupies (e.g., when a disembodied, anonymous algorithm is used to support classroom teaching), people are likely to react negatively. 

Who Is Interacting with the Algorithm? 

Different people may have more or less aversion depending on their role relative to the algorithm. For example, when a robot replaces a bartender (Hochberg, 2016), the customers of the bar and human bartenders are likely to have different reactions. Research suggests that a bar patron would dislike having their favorite bartender replaced by a robot (relative to another human) because people value human (vs. robotic) labor in contexts of consumption (here, consuming alcohol) where interpersonal relationships are important (Granulo et al., 2021). On the other hand, another bartender may prefer being replaced by a robot more than by another person, as being replaced by a robot triggers fewer identity-relevant social comparisons (Granulo et al., 2019). 

What Task Is the Algorithm Performing? 

Also important for determining algorithm aversion is the specific nature of the task that machines are intended to perform. For example, an algorithm’s function could be to make forecasts. However, the subject matter of the forecast could be more objective (the performance of financial markets) or subjective (how funny people will find different jokes). Research suggests that because we consider machines to be better at objective rather than subjective tasks, people are more likely to experience algorithm aversion in a subjective (rather than objective) task context (Castelo et al., 2019). 

Another critical attribute of machine task performance is whether the task involves morality. For example, a machine’s function could be to make a forecast that is objective, but varies in how morally charged (how to sort people in a hospital through medical triage) or morally neutral (predicting which stocks to invest in) it is. As noted in the section on morality, people generally dislike algorithms making moral decisions (Bigman & Gray, 2018), but seem more comfortable with such contexts when machines’ impartiality (relative to humans) is salient (Bigman et al., 2021). 

Explanations for Algorithm Aversion  

Expectations of Perfection 

People have heightened expectations for machine performance (Lee & See, 2004; Lyons & Guznov, 2019), believing that machines will almost always decide correctly, given that they are free from human bias. Unfortunately, machines are seldom perfect and often produce their own biases (given that they operate based on data generated by biased humans). AI systems are typically quite “brittle,” demonstrating competence in narrow and well-defined domains (Falk, 2021). An algorithm may be good at helping to predict skin cancer from well-controlled photos of people with the same skin tone but lack the generalized intelligence to perform well across people of different skin tones (Falk, 2021). 

Because of expectations of perfection, people show initial excitement in using algorithms but shun them quickly once they observe their limitations. In one classic study, people responded to algorithmic forecasting versus human forecasting across two tasks: predicting the success of MBA applicants based on their resumes and guessing the number of airline passengers departing from different states. Participants could choose to tie their incentives to future predictions made by algorithms or by themselves after observing algorithmic and human forecaster performance (Dietvorst et al., 2015).  

Despite algorithms being more accurate, participants significantly preferred relying on forecasts made by themselves or another human. Importantly, algorithm aversion was strongest when participants had previously seen the algorithm perform. This was because participants observed that algorithmic predictions are, much like human predictions, imperfect. People’s heightened expectations for algorithmic (vs. human) performance meant they were less tolerant of algorithmic error than human error. 

Other studies also support the importance of expectations of perfection in algorithm aversion. People rate algorithmic advice as less accurate when algorithms take longer to generate their forecasts (Efendić et al., 2020), whereas people rate slowly generated forecasts from humans as more accurate. This asymmetry results from the finding that people perceive response times as indicative of effort taken to generate a forecast, associating longer times with higher effort. When humans take a long time to generate a forecast, people perceive them as being more deliberate and careful, because predictive tasks are considered difficult for humans. However, because people expect algorithms to handle predictive tasks quickly and easily, they interpret long forecast generation times as indicating that the algorithm is struggling. 

Other work shows that not only do people disproportionately penalize mistakes made by algorithms, but also they do so especially when mistakes occur in the decision-making process as opposed to in the later stages (Manzey et al., 2012). When expectations of perfection are violated earlier, they produce more algorithm aversion compared to when they are violated later in one’s interaction with algorithms. Related work has also shown that people are less likely to use algorithms for forecasting in uncertain domains the more the algorithm deviates from near-perfect results (even if the algorithm is more accurate overall compared to alternatives; Dietvorst & Bharti, 2020). 

One way to reduce aversion to algorithms when algorithms err is to show people evidence that algorithms can learn from their mistakes. People accepted less advice from a familiar algorithmic forecaster (compared to a human forecaster), demonstrating standard algorithm aversion, but this effect was mitigated when observers learned that the algorithms were able to learn and improve on their previous performance (Berger et al., 2021). Other work suggests that many consumers distrust algorithms because they mistakenly believe that algorithms, unlike humans, cannot learn from their mistakes (Reich et al., 2022). However, highlighting algorithms’ ability to learn could help overcome this specific aversion. 

Apparent Lack of Emotion 

As discussed, people see machines as lacking the mental capacity for experience, including sensing and feeling. This perception can help explain algorithm aversion in domains where people think success revolves around the ability to feel emotions, such as the subjective task of providing dating advice (as compared to the objective task of providing financial advice) (Castelo et al., 2019). 

Consistent with an aversion to apparently-emotionless machines performing emotion-laden tasks (Waytz & Norton, 2014), people are more willing to assign a relatively objective task (doing arithmetic) to a standard mechanistic robot compared to a more subjective task (judging people’s emotional states; Wiese et al., 2022). People are also more likely to assign the subjective emotional task to a more emotionally capable robot. Interestingly, participants also assigned the arithmetic task to an emotionally incapable robot (Wiese et al., 2022), suggesting that people believed there was a trade-off between mathematical skills and social skills in machines. 

People also prefer humans (vs. robots) to provide services with a higher symbolic value (e.g., getting a personalized tattoo), given that the unique emotional expression involved in receiving such services is not often associated with robotic labor (Granulo et al., 2021).  Similarly, people trust humans more than machines to perform tasks such as hiring and evaluating others’ work (but trust algorithms and humans equally for more analytical tasks) partly because they perceive algorithms to lack intuition and subjectivity (i.e., emotional experience; Lee, 2018).  

People’s perceptions that machines lack emotion also explain their aversion to machines making moral decisions that require compassion and concern (Bigman & Gray, 2018). Consumers dislike algorithms making moral tradeoffs (e.g., deciding whether an insurance company should cover a patient’s prescription) because they view algorithms’ decisions as caring only about objective consequences rather than the emotions of those involved (Dietvorst & Bartels, 2022). People show increased algorithm aversion, especially in prosocial contexts (e.g., making peer-to-peer microlending decisions for entrepreneurs in the developing world) where empathy is more important (Heßler et al., 2022). Algorithm aversion also tends to be higher when decisions involve identifiable recipients of resources because such recipients evoke greater empathy, a capacity people see algorithms as lacking (Yang & Teow, 2020). 

The apparent lack of emotional experience in machines is not always a liability, however. People are more willing to have their behavior at work tracked by an algorithm than by humans, in part because machines’ perceived inability to experience emotions makes them seem less judgmental (Raveendhran & Fast, 2021). In a related study, HR managers were more likely to delegate an unpleasant task, such as dismissing an employee, to an algorithm, but were more likely to delegate a pleasant task, such as promoting an employee, to a human (Maasland & Weißmüller, 2022). The former task entails a greater emotional burden, which algorithms are expected not to experience. 

(Over)Confidence and Expertise 

Overconfidence and perceptions of expertise are other explanations for algorithm aversion, especially when it comes to advice from algorithms. People overestimate their own expertise and the accuracy of their judgments, and dismiss other sources of advice (Soll & Mannes, 2011). Initial studies on algorithm aversion had participants choose between an algorithm's forecast and their own, and suggested that simple self-confidence may have led them to prefer their own forecasts (Harvey & Fischer, 1997). Consistent with this idea, when people are unable to rely on their own forecasts, they become less algorithm-averse (Logg et al., 2019). 

Work also demonstrates that experts show higher algorithm aversion levels than employees with moderate expertise in the same domain (Allen & Choudhury, 2022). Experts are more likely to ignore advice from AI even when it hurts their performance (Logg et al., 2018) because they have confidence in their own judgments. Consistent with this study, top-ranked sales agents were most resistant to AI coaching (Luo et al., 2021), and people who score higher on self-confidence rely on machines less (Lewandowsky et al., 2000). 

Responsibility Without Control 

Relying on algorithms takes control from human decision-makers, and people often dislike relinquishing control. Work finds that giving people some control to modify AI forecasts helps to reduce algorithm aversion (Dietvorst et al., 2018), even if the amount of control is small. Part of the reason people dislike giving up control to algorithms is that people still feel responsible for the outcomes of algorithm decisions (Allen & Choudhury, 2022). In one study, patients considering surgery were more likely to follow a physician's recommendation than a computer's because they felt more personally responsible when the algorithm decided (Promberger & Baron, 2006). When patients could place the responsibility on another human agent (the doctor), they felt less personally responsible for the outcome of the surgery. When relying on the algorithm (absent a physician’s advice), people still seek a human to hold responsible for the medical outcome (Gray & Wegner, 2010). In this case, the (uncomfortable) responsibility falls on the person taking the advice. 

Opacity and Lack of Transparency 

Despite society’s reliance on algorithms to do many things, people largely do not understand how they work or make decisions (Burke, 2019; Lepri et al., 2018; Mittelstadt et al., 2016; Shin, 2021). They also dislike relying on something that seems opaque for important decisions. They will not accept the “what” of the decision without knowing the “how”—how the decision was made. One study found that people prefer human joke recommenders to algorithmic ones in part because they struggled to articulate how the algorithmic model generated their results (Yeomans et al., 2019), but explaining how the algorithms worked to people later increased their reliance on them. Other studies show that people required to interact with algorithms try hard to understand how they work—though with varying success (Lebovitz et al., 2022; Rahman, 2021; Waardenburg et al., 2021).  

Recently, scholars have differentiated between technical opacity, where people fail to understand the inner mechanisms of algorithms, and organizational opacity, where the inner workings of algorithms are hidden from the public due to an organization’s strategic interests (Jarrahi, 2018; Walmsley, 2021). Most of the work on algorithm aversion has examined technical opacity because AI, such as machine learning and neural networks, is incredibly complex and not easily understood by people (Shrestha et al., 2021). Additionally, many machine learning techniques involve algorithms learning without human supervision, such that even the programmer may not fully understand the decision-making processes of the algorithms (Koo et al., 2015). 

Opacity helps drive algorithm aversion because people are more likely to trust and use forecasts when they understand how they work (Herlocker et al., 2004; McNee et al., 2006), and people have very little idea of how machines operate. Opacity is especially likely to increase algorithm aversion for decisions around fairness because transparency is an important element of fairness (Konovsky, 2000). Decreasing opacity is critical because once algorithms are developed and integrated into organizations, people rely on them heavily and take for granted that their decision-making processes are sound (Newman et al., 2020). The AI community has become so concerned with illuminating how algorithms work that a sub-field of AI, explainable AI (XAI), was born precisely to make extremely complex models more understandable to people (Adadi & Berrada, 2018). 

However, work shows that reducing opacity may not be as simple as describing how algorithms make decisions. Being provided with rich explanations of how algorithms work can be overwhelming, which can decrease trust in algorithms (You et al., 2022) and increase anxiety about relying on algorithms in self-driving cars (Shariff et al., 2017). Other work shows that if algorithms are accurate enough, opacity may be less of a concern to people. One large-scale survey of public attitudes toward artificial intelligence found that people care more about the accuracy of AI’s decisions than being able to explain those decisions (Nussberger et al., 2022).  

TRUST AND ENGAGEMENT 

Trust is one of the most important considerations when people work together (De Jong et al., 2016), and as machines come to replace people, increasing trust in them is critical. Psychological research on trust (Yakovleva et al., 2010) finds that trust hinges upon the features of the person doing the trusting (the perceiver) and the person receiving the trust (the perceived). Research on people’s trust in machines has largely illustrated the importance of the perceived—the machine—with one meta-analysis concluding that machine performance is the most influential factor affecting trust, such that people trust machines that perform proficiently (Hancock et al., 2011). Beyond performance, research highlights other important drivers of affective trust (e.g., rapport) and cognitive trust (e.g., confidence) in robots and AI (Glikson & Woolley, 2020).  

Similarity to Humans 

Despite the uncanny valley, people generally trust machines more when they behave and look like the humans they are designed to replace. They trust robots more when robots follow appropriate norms of spatial distancing and gaze and engage more with robots that display humanlike body language (Mumm & Mutlu, 2013; Kidokoro et al., 2013; Liu et al., 2013). People are more attracted to and interact more with robots that move organically and naturally (Kondo et al., 2013). Museum visitors like, trust, and respond more to a robotic museum guide that can communicate in a more human-like way (Bickmore et al., 2013). Humanlike displays of empathy also enhance trust in robots. In one experiment, a robot played chess with two humans and displayed empathetic responses to one while acting neutral toward the other (Leite et al., 2013). These empathetic displays lead people to see the robot as friendlier, suggesting that empathy can build trust in robot-human interactions.   

Research on anthropomorphism has also found that human likeness can enhance trust. For instance, elderly participants find human-like robots more pleasant and trust them more, and these interactions with anthropomorphized robots can even produce positive physiological reactions (Zhang et al., 2010). People also trust an autonomous vehicle more when it is anthropomorphized (given a voice and a name) and blame it less for erring than a non-anthropomorphized autonomous vehicle (Waytz et al., 2014). The “mere” humanness of the machines used in these studies was likely insufficient to trigger the uncanny valley effects found for eerily human robots (Złotowski et al., 2016). 

Human-Machine Matching 

Human beings adapt dynamically to the behavior of other human beings, and research finds that people prefer robots that adapt to their own behavior. In one study, people preferred a robot that matched their level of extraversion by reciprocating the amount of eye contact they gave (Andrist et al., 2015). Other research shows that people prefer an online recommendation agent that matches their extroverted or introverted personality (Nass & Lee, 2001) and that people respond more positively to a GPS that matches their mood (e.g., conveying a happy voice when drivers are happy; Johnsson et al., 2005). Competitive people also prefer to work with competitive robots compared to relationship-driven robots in a card game, whereas the reverse was true for less competitive people (Correia et al., 2017). Trust seems to depend on social alignment between the user and the robot. 

Social Context 

People often feel safer in numbers and feel more comfortable interacting with robots when they are within groups of people (Michalowski et al., 2006; Booth et al., 2017). In one study, college students were more likely to let a robot into the restricted area of their dormitory when the students were in groups (Booth et al., 2017). Another study found that people interacted more with worker robots when they were with other people (Michalowski et al., 2006). 

Tangibility 

The tangibility of machines (the ability to perceive or touch them) is positively related to cognitive trust (Li, 2015), with robots representing the most tangible forms of machines, followed by virtual agents, and then algorithms. For instance, people are more likely to comply with an unusual request made by a robot than by an identical 2D image presented on a screen (Bainbridge et al., 2011). However, task presentation can moderate these effects such that the positive effects of tangibility occur for tasks presented in physical spaces but not for tasks presented on a screen (Shinozawa et al., 2005). 

Research also suggests that people trust virtual AI more when it has an identity. For example, people responded more positively to feedback from an agent with a robotic picture as an avatar than from an agent without any visual representation (Mumm & Mutlu, 2011). People also find feedback delivered by robots to be more trustworthy than the same feedback delivered on a computer screen (Shim & Arkin, 2014). Similarly, participants perceive recommendation agents with more tangible virtual identities to have increased social presence, thereby increasing affective trust (Qiu & Benbasat, 2009). However, other work has demonstrated considerable complexity in the interactive effects of machine tangibility, performance, and perceiver characteristics on trust (de Visser et al., 2016; de Visser et al., 2017), making this a fruitful area for future research.  

Reliability 

Reliability, the extent to which machines display consistent and expected behavior across time, also importantly determines trust in technology (Hoff & Bashir, 2015). In high-risk situations, people lose cognitive trust in robotic advice when robots make mistakes (Robinette et al., 2017). Other research suggests that early lapses in reliability are especially likely to undermine trust compared to later lapses (Desai et al., 2013). 

Task Characteristics 

Task characteristics refer to the nature of a machine’s work, and considerable work shows that people trust machines more to perform tasks that match their perceived strengths (Waytz & Norton, 2014). For example, in teams with both human and robot members, humans tended to trust robots over human teammates to schedule tasks and manage workflow, tasks which involved complex analyses and optimization, as the nature of the task was consistent with the robot’s perceived strengths (Gombolay et al., 2015). Other research has examined differences in trust based on domain expertise (Gaudiello et al., 2016). This work presented participants with ambiguous questions with functional content (e.g., which of two objects is heavier) or more socially relevant content (e.g., which of two objects is more socially appropriate to bring to a swimming pool). After the participants responded, a robot confederate provided contradictory advice, and participants indicated whether they trusted the robot’s advice enough to change their responses. Participants were more likely to trust the robot’s advice on functional issues than social ones, suggesting perceived expertise in the former domain compared to the latter.  

Interpersonal Closeness 

Behaviors that promote perceived interpersonal closeness between people and machines (Mehrabian, 1967) tend to increase trust (except when these behaviors appear intrusive or suspicious, Burr et al., 2018). This includes social gestures such as active listening, responsiveness, and proactivity, all of which increase perceptions of interpersonal closeness. Such gestures not only increase people’s cognitive trust in robots, but also increase perceptions of the robot’s mental capacity (DeSteno et al., 2012). One experiment demonstrated that robots incurred more trust when they provided support in a way that engendered closeness (de Visser & Parasuraman, 2011). In a stable support condition, the robot always offered participants assistance whether it was needed or not. But in an adaptive support condition, the robot offered assistance only during difficult tasks. Participants in the adaptive condition reported higher levels of trust in the robots because the robots could only offer assistance when appropriate, again highlighting the importance of responsiveness for trust. 

Other work shows that behaviors that generate human disclosure toward robots can engender trust. One study had a virtual agent ask participants either personalized or generic questions and found that participants trusted the robot more when it asked personalized questions and gave more personalized recommendations. In addition to robotic behaviors facilitating closeness, other research suggests that human behaviors can also increase perceptions of closeness. For instance, the act of assembling a robot leads people to feel psychologically closer to the robot in terms of trust and viewing the robot as a team member (You & Robert, 2019). 

Simply demonstrating the capacity for social closeness can increase trust. For instance, people liked, trusted, and were more willing to spend time with robotic dogs that engaged in behaviors indicating social intelligence, such as social gestures, responsiveness, active listening, back-channeling, and learning (Lee et al., 2005). Responsive virtual agents (compared to less responsive agents) elicit more positive emotions, such as emotional trust from users. In one study, participants felt more emotionally connected to a virtual, 2D agent capable of hand gestures and expressive facial movements than one that was not (Matsui & Yamada, 2019).  

Transparency 

Transparency refers to the extent to which the observer understands the operating rules and inner logic of artificial agents, and is another key factor in building trust in technology (Hoff & Bashir, 2015). For example, explaining to people how AI makes decisions increases their perceptions of the technology as competent and benevolent (Pieters, 2011; for a more thorough review, see Xiao & Benbasat, 2007). Other work showed that explaining to people how AI works reduced the negative effects of having one’s expectations violated by an algorithmic peer-reviewing system (Kizilcec, 2016). Explaining the algorithm’s underlying logic helped to mitigate the potential distrust. 

The factors that contribute to trust in robots and artificial intelligence are diverse and likely to interact with each other, making them a good avenue for future work. 

MACHINES IN DIFFERENT SOCIAL ROLES 

This chapter generally focuses on how people make sense of machines across contexts, but it is also important to overview the applied contexts that feature machines.  

Service Industry  

Some of the first integrations of robots into the workplace occurred in the service industry, with machines taking the roles of restaurant servers, bussers, and hosts. More advanced robots have begun to take more sophisticated service roles that require complex communication. In one lab study that examined responses to robots as frontline hotel workers, customers evaluated robots as similarly effective as human workers (Kim et al., 2021). Customers were also pleasantly surprised by the robots because they surpassed their expectations. Other work shows that people enjoy being served by robots in a hotel setting but are less forgiving when service robots err compared to their human counterparts (Yam, Bigman, Tang, et al., 2021). 

Education and Childcare 

Considerable work has assessed the performance of robots in educational contexts. Robots offer educational advantages over other technology (e.g., algorithms) because of their embodiment and social presence. Some work shows that people learn more from a physically present robot than from video or audio instruction (Leyzberg et al., 2012). Robots assist in learning even more when they exhibit socially supportive behaviors, indicating that their appearance as social agents can increase their effectiveness (Saerbeck et al., 2010). Other work shows that a humanoid robot in a childhood education center that displayed variability in behavior produced more engagement. Children were disengaged when the robot behaved predictably (i.e., robotically). However, children's interactions with the robot improved when it displayed a full repertoire of behaviors, including using hand gestures, dancing, and lying down (Tanaka et al., 2007). 

One limitation of robots in educational settings is that in group contexts, the presence of other humans can overshadow the robot. Children retain less information from a storytelling robot when in a group than in an individual setting (Leite et al., 2015; Leite et al., 2017). Further, children are less forgiving of robot mistakes in a group context than when interacting with the robot one-on-one (Baxter et al., 2013). This may indicate that people perceive humans as more social than robots, so the presence of other humans lowers robots’ relative social standing. 

Social robots can also assist with children’s social development. Research shows that robots can ask questions that facilitate cooperation between children (Strohkorb et al., 2016) and that robots can help children on the autism spectrum learn to engage socially with others (Scassellati et al., 2018; Kim et al., 2012). This work shows that as robots become more advanced social agents, they can also help improve people's social lives. 

Teamwork 

Much social and organizational behavior psychology research has focused on integrating robots into the workplace as teammates for team projects. In teams where robots are collaborating with humans rather than directly replacing all of them, robot social behaviors can enhance team functioning and lead to more positive team member perceptions.  

The physical presence of a robot can elicit social facilitation effects that motivate people to solve simple tasks and increase pressure to perform well on complex tasks (Riether et al., 2012). This makes robots valuable coaches and assistants. For example, older adults preferred working with an embodied robot exercise coach over a virtual coach, finding the experience with the embodied robot more enjoyable and helpful (Fasola & Matarić, 2013). Robots also tend to receive more respect and compliance from people than disembodied machines because of their physical presence (Bainbridge et al., 2011).  

Robots on human-robot teams can influence group dynamics. Whereas traditional robots often assisted with physical labor, modern robots can fill highly social roles like guiding team communication. Robots can help their teams by guiding group conversations (Mutlu et al., 2009) and facilitating turn-taking by using social cues like gaze (Skantze, 2016; Skantze, 2017). They can even detect participation imbalances and invite people who have not spoken to speak (Skantze, 2017). Acting in a facilitator role is a sophisticated task for a robot to perform but can be useful given that people may perceive robots as more impartial than humans in such a role. 

In addition to facilitating group communication, some research suggests that robots can help manage team conflict. Jung et al. (2015) tested how human teams responded to a robot denouncing a personal attack carried out on a confederate. They found that these teams were made more aware of the attacks after the robot called attention to them, prompting members to deal with the situation rather than ignore it. Mediator robots can also help children learn to resolve interpersonal conflict (Shen et al., 2018).  

Robots can also enhance team functioning through their own communication. Just as individual members within teams can change group tone, so can individual robots. One experiment tested the effect of robot backchanneling on a team and found that signs of active listening improved team functioning (Jung et al., 2013). The robot inspired a norm of engagement that humans adopted as well. This influence over teams demonstrates that robots can be perceived as fellow team members and instill norms within a group just like a person.  

Machines can also serve as role models for teammates by expressing vulnerability through sharing fears and doubts. When robots made vulnerable statements to groups (e.g., admitting their mistakes), team members in these groups talked with each other more, were more egalitarian in speaking time distribution, and held more positive regard toward their group (Traeger et al., 2020). In other words, by modeling openness, the robot could facilitate norms of openness and dialogue within a human group. In another study, a robot that displayed vulnerability inspired a more synergistic team culture in which the group alleviated tension by consoling team members, laughing together, and explaining failures as they arose (Sebo et al., 2018).  

Robot body language can also elicit feelings of belongingness on a team. Robots that orient their gaze and position toward a group produce feelings of inclusion (Vázquez et al., 2017). This work suggests that people prefer working with robots that display humanlike social cues, demonstrating that people prefer social robots as teammates. Other work more directly supporting this point found that robots that express group-based emotions are seen as more trustworthy and are perceived to have higher group identification (Correia et al. 2018). This finding suggests that people prefer robots that exhibit human behaviors of group belongingness in team settings which may result from people viewing a robot teammate as assisting rather than replacing them.  

Robots can also induce social conformity in team settings. A study using a robot-human team to replicate Asch’s (1955) class social conformity experiment found that robots are almost as effective at inducing social conformity as humans (Qin et al. 2022). This work suggests that people often see robots as full-fledged social agents—and hence a potential force for both good and bad on teams.  

Healthcare 

Robots are increasingly used in healthcare. One area where healthcare robots have thrived is increasing people’s compliance with healthcare routines. In one experiment, patients complied and engaged more with relaxation instructions from a robot than from a computer (Mann et al., 2015). Robots also help increase compliance with medical routines at home. In one study, people tracked their health metrics for twice as long and formed a greater bond when they had a robotic weight coach than a computer or paper log (Kidd & Breazeal, 2008). This finding shows that embodied robots inspire greater social influence than disembodied machines. 

Robots in these contexts appear more effective when they follow social norms, even at the cost of efficiency. For example, older adults prefer a robot to request help from them and then return the favor, following social reciprocity norms (Lammer et al., 2014). Such behavior may make people feel more connected with robots or feel they have more agency over the robots. 

However, despite the benefits of machines in some healthcare settings, people prefer humans to make medical decisions because those decisions are moral. People have strong feelings about robots and morality.  

THE MORALITY OF MACHINES 

Questions about morality have always taken center stage when people think about robots and AI. In the classic book I, Robot, Isaac Asimov (2004) imagined how robots might be programmed to solve conflicting moral imperatives, like when the owner of a robot commands it to kill someone. On the one hand, individuals want their robots to be obedient, but on the other hand, society would rather not have people owning armies of murder bots. Asimov imagined three laws for robot morality. The first was to not harm people, the second was to obey your owner, and the third was to act in self-preservation. Importantly, the earlier laws take precedence over the later ones, meaning that a robot would disobey its owner if commanded to kill another person and would destroy itself rather than disobey its owner. 

Although these laws are fun to ponder, in practice, programming morality into robots is far more difficult because morality is too complex to be captured by three simple heuristics. Questions of harm are matters of perception (Schein & Gray, 2018) and often involve tradeoffs. The same act may harm some people while helping others, and it is unclear whose harm people should prioritize. If one person’s misery or death helps to enable the flourishing of others (Le Guin, 1993), is that a moral or immoral act? Philosophers have struggled with this dilemma for centuries (Bentham, 1776; Kant, 1785), so programming a quick answer into robots presents a challenge. Researchers have begun exploring these questions merely by surveying how people generally want machines to respond in moral dilemmas. 

But even beyond the difficulties of answering how machines should make moral judgments in moral dilemmas, there are deep questions about whether robots and AI even belong in the category of moral entities. Does it make sense to talk about a machine as truly evil? Or as heroic? And should machines enjoy moral protection? Should people care about machines’ suffering? The answers to these questions depend on what kind of mind people perceive in machines. 

Moral Minds 

Moral Agents 

One of the main reasons people care about who has a mind is because some minds imply that certain entities are moral agents—entities perceived as capable of making decisions, both right and wrong. Moral agents are not only perceived as capable of acting with evil (or good) consequences, but they are perceived as legitimately good or evil. Generally, to be seen as a moral agent requires being ascribed the capacity for agency, the dimension of mind that includes self-control, planning, and memory (Gray et al., 2007). We generally see adults as having more agency than children, explaining why we hold adults more morally responsible for their behavior. We will imprison adults for the rest of their lives and sometimes execute them for wrongdoing, but we seldom do the same to children. Likewise, if an animal does something bad, like mauling someone, we seldom hold that animal morally accountable, even if we euthanize the animal.  

People seem to see robots as somewhat moralized agents, but do not apply the same moral standards to them as to humans. For instance, people hold a human more responsible than a robot for the same mistake (Khan et al., 2012). People also feel more outrage toward human discrimination than algorithmic discrimination (Bigman et al., 2022). One reason for these discrepancies is likely that people ascribe less intentionality and free will to robots (Bigman et al., 2019). Additionally, although some work shows people accept robots that sacrifice one person for the good of many (Malle et al., 2015), other work shows people oppose algorithms making moral trade-offs because algorithms use overly consequentialist decision strategies (Dietvorst & Bartels, 2022). Ostensibly, people see robots as less feeling and more deliberative than humans and therefore expect them to make colder, more rational decisions. 

Although people may want moral agents to be fully rational—like machines—people also want moral decision-makers to have some sense of compassion.  Philosophical discussions of moral agency emphasize agency-related mental capacities (Hume, 1751; Kant, 1788; Locke, 1836), but they also emphasize experience-related mental capacities like emotion, empathy, and the ability to feel pain or pleasure (Coeckelbergh, 2010; Himma, 2009; Hume, 1751). These experience-related capacities are important because people expect moral decision-makers to care about the targets of their decisions, or at least implicitly appreciate the stakes of moral decisions. When a judge grapples with a decision about parole or a doctor wrestles with a decision about whether to perform risky surgery, people want to know that these moral agents understand the weight of their decisions and how they affect the people involved. 

As biological entities, people see most human beings (and animals) as having a basic capacity for experiencing pain and pleasure, unlike robots. Without perceiving the ability of robots and artificial intelligence to feel emotion, people may not want them to make moral decisions.  

Aversion to Moral Machines  

In many domains, people dislike the prospect of AI making moral decisions. For example, people prefer humans relative to AI to decide whether a patient should undergo a risky surgery, whether a drone should fire missiles, and whether an inmate should be granted parole (Bigman & Gray, 2018). This aversion occurs whether the outcome of these decisions is positive or negative, for example, whether a patient survives or dies in surgery. In fact, people’s aversion to machines making moral decisions even leads them to sacrifice tangible gains to ensure that humans make such decisions instead. In one study, participants learned that implementing an AI system for making medical decisions would save money to provide more hospital beds for patients, and then indicated how many new beds would have to be provided for them to endorse the AI system (Bigman & Gray, 2018). Although people became generally more open to the system as the number of beds increased from 10 to 25 to 50, almost half the participants refused to endorse an AI medical decision-maker even if it saved 100 beds (see Figure 4). They were categorically opposed to the idea of machines as moral decision-makers.  

Figure 4: Number of Beds Gained by Using AI and Preference for Human Over AI Doctor  

Note: Many people prefer a human doctor to an AI algorithm, even when using the AI would result in additional hospital beds. Created from supplemental data from “People are Averse to Machines Making Moral Decisions,” by Bigman & Gray, 2018, Cognition, 181, 21–34.

A major reason people oppose AI making moral decisions in medical settings is their belief that AI does not account for the unique characteristics of humans in its decisions, instead only following strict, programmed rules. People view themselves as unique beings, each with individual circumstances and needs. The perceived inability of AI to consider this in its decision-making process thus creates resistance to AI recommendations. Indeed, individuals who perceive themselves as more unique tend to be more strongly resistant to medical AI. However, if the care provided by the AI is personalized to each patient's unique case and symptoms, this resistance dissipates (Longoni et al., 2019). Related work shows that people prefer humans for moral decision-making, believing humans are more capable than machines of judging each individual case flexibly (Jauernig et al., 2022). 

This desire for flexibility and attentiveness to unique human circumstances could also explain why people oppose using algorithms to hire and promote workplace employees (Newman et al., 2020). Despite having advantages like eliminating human bias in the selection process, people perceive these algorithms as reductionistic, failing to consider qualitative or contextual information about the candidates. 

Openness to Moral Machines  

Despite general opposition to using machines for moral decisions, there are certain instances where people prefer machines to make moral decisions, particularly around issues of algorithmic bias. Algorithmic bias represents the general phenomenon of algorithms reproducing biased decision-making processes and outcomes because they are trained on data generated by humans that inevitably contains racial and gender biases (Caliskan et al., 2017). Despite the real existence of algorithmic bias, this bias tends to be easier to mitigate than human bias (Obermeyer et al., 2019), and people assume (often correctly) that machines are generally less racist and sexist than human decision-makers (Gates et al., 2002; Kleinberg et al., 2017, 2018).  

Interestingly, people become more open toward algorithms as moral decision-makers when reminded that humans are prejudiced. When participants learned that doctors were making prejudiced triage decisions about who deserves medical care during the COVID-19 pandemic, they became more accepting of machines making those decisions (Bigman et al., 2021). Similarly, people are more accepting of algorithmic decisions than human decisions that produce gender or racial disparities because they believe that algorithms are blind to the personal characteristics of those they are judging (Bonezzi & Ostinelli, 2021). People also express less outrage toward algorithms that produce sexist decision outcomes than humans that produce such outcomes because they attribute less prejudiced motivation to algorithms compared to humans (Bigman et al., 2022). 

People also hesitate to implicate machines in moral wrongdoing because they do not always fully attribute agency to them. In a study of reactions to a machine engaging in some unethical behavior, such as making racist decisions, even when people opposed the decision, they did not always deem it to be truly immoral (Shank & DeSanti, 2018). Such judgments of moral wrongness were contingent on perceptions of the machine’s mind, which varied across participants (rather than people viewing machines as uniformly agentic).  

What would it take for machines to be fully moral agents? One framework suggests some specific elements necessary for people to imbue machines with moral responsibility (Bigman et al., 2019). These elements are important because machines can increasingly cause harm, not only in medical triage decisions but also in military applications such as drones and autonomous weapons systems. Clearly, people see robots with more general autonomy—or agency—as more morally responsible for their actions, but autonomy and agency can be deconstructed into more specific factors. 

The first element for ascribing responsibility to machines is situation awareness. When people believe that machines can appreciate the meaning of their behavior, they are likely to ascribe more responsibility to them. If a drone can appreciate that firing a missile is not just releasing payload, but instead blowing up a compound, people will likely ascribe more responsibility to it.  

Situation awareness can be understood through the lens of action identification (Michaels et al., 2013), which suggests that people make sense of different actions at lower levels (concrete behaviors) versus higher levels of meaning underlying the behavior (Vallacher & Wegner, 1987). For example, a low-level understanding of the behavior of “getting married” is saying words in front of a preacher, whereas a high-level understanding of “getting married” is committing to a partnership to make each other better people. People will hold machines accountable to the extent they believe machines have a high-level identification of behaviors, can see the meaning of their actions, and recognize that firing a shell might also represent “murder.” 

The second element for ascribing machine responsibility is the perception of free will. Specifically, free will can be understood as the ability to “do otherwise” (Franklin, 2015). If a machine is perceived to have the ability to not cause harm, then it will be held more accountable if it does cause harm. Free will also relates to intentionality, an important element for holding people responsible. People are held more accountable for acts they do intentionally, but for intentions to matter, people must have both the ability to act on them and the ability to not. 

The third element for ascribing machine responsibility is the appearance of the robot and how much perceived agency it conveys. As reviewed previously, machines that appear more humanlike are ascribed more human minds (i.e., a higher degree of agency) and more moral capacities (Waytz et al., 2014). If two machines caused the same amount of harm (e.g., killing civilians), the machine that looked more humanlike would seem more responsible and likely nefarious.  

The fourth element for ascribing machine responsibility is the potential harm it can cause. People generally expect the suffering caused by a harmful act and the amount of intention behind that act to be proportional (Gray et al., 2014). It is easy to believe that a scraped knee was caused accidentally, but harder to believe that the death of three people was an accident. If a machine manages to kill many people simultaneously, its actions will seem more intentional—and people will hold it as more responsible—than if it simply injures a few people. 

An open question is what happens if people see machines as responsible for a moral transgression. Work on robot culpability can be divided into work examining whether people think robots should be blamed for causing moral harm and whether people think robots should be punished or even held criminally liable (Hallevy, 2010a, 2010b). In the event of moral violations, people often look for an entity to blame (Jensen, 2010). When technology contributes to a moral violation, its status as a moral agent and thus how blameworthy or punishable it is remains ambiguous. This contributes to a retribution or punishment gap where moral harm occurs, but neither robots nor their developers or manufacturers are appropriate subjects for blame (Danaher, 2016). For example, people wish for electronic agents to be punished for legal offenses, despite those punishments (such as imprisonment) being impractical (Lima et al., 2020). 

Other work finds that when AI commits a moral violation, people are more likely to blame the AI itself rather than its programmer, organization, or users (Shank & DeSanti, 2018). Research also shows that people struggle to assign blame for machine violations of specific moral foundations compared to human violations (Maninger & Shank, 2022), suggesting again that people ascribe less moral agency to machines than to humans. Other research found that people perceive machines to have less moral capacity than humans, albeit with some moral responsibilities and rights (Gamez et al., 2020). 

A related issue to whether machines receive blame and punishment is how people attribute shared blame when humans and AI jointly make poor moral decisions. People tend to blame humans more in these joint moral violations, regarding human mistakes as less permissible than AI mistakes (Shank, DeSanti, & Maninger, 2019). People also attribute blame differently depending on the role of the human relative to the AI. For example, people blame a human overseeing the AI less than someone acting on recommendations received from the AI. 

In general, people want robots punished when robots violate moral norms. However, gaps between people’s intentions and behaviors (Loewenstein, 1996) call into question whether people actually punish robots in interactions with them. Recent work has examined how people react to abusive, negative feedback from machines, specifically robot bosses. This work found that people punished anthropomorphized robot supervisors more harshly than non-anthropomorphized robot supervisors when receiving negative feedback (punishment was operationalized by powering down the robot) (Yam, Goh, et al., 2022). People perceived anthropomorphized robots as more agentic and thus more abusive in their negative feedback, facilitating stronger punishment. Other research examined physical versus virtual representations of robots, showing that people punished (by powering down) embodied robots more than those shown on screen. However, these effects were moderated by participants’ power and sense of threat (Keijsers et al., 2021).  

Machines as Victims? 

Much of mind perception research reveals that people deny pain to robots (de Graaf & Malle, 2019; Gray et al., 2007; Weisman et al., 2017), potentially explaining why people feel that impoliteness (Rehm et al., 2013) and bullying behavior toward robots (Brščić et al., 2015; Salvani et al., 2010) are permissible. However, some contexts elicit attributions of robot suffering, which is increasingly true as technology becomes more humanlike in appearance, behavior, and expression. Examining this phenomenon involved people’s reactions to requests to harm machines (Bartneck & Hu, 2008). In this study, participants were asked to turn off a robot, but as soon as they approached it, it began to beg for its “life,” causing participants to resist going through with the act. One participant even exclaimed, “This is inhumane!” 

People also cooperate with robots as though they were humans. For example, in a prisoner’s dilemma game, participants assigned to robot partners reciprocated prosocial behavior to the robots and did so more if the robots behaved cooperatively instead of randomly first (Sandoval et al., 2016). This work again suggests people do not seek to victimize robots indiscriminately. 

Children also seem particularly prone to considering machines’ potential for suffering, likely because they have not yet developed the sense that machines lack experience and emotion (Brink et al., 2017). In interviews with children who interacted with a social robot, all said putting a broom in the closet was morally permissible, but less than half said the same about the robot (Kahn et al., 2012). Other work shows that although children do not consider robotic dogs alive, they still grant moral standing to them (Melson et al. 2009).  

Other studies suggest that perceptions of mind in machines predict the ascription of both moral rights and responsibilities (see also Floridi & Sanders, 2004). For example, one study assessed individual differences in anthropomorphism, and high anthropomorphizers expressed more moral disapproval toward destroying IBM’s legendary chess-playing computer, Deep Blue (Waytz, Cacioppo & Epley, 2010). Other work shows that humanlike robots elicited more care and empathy from people than machinelike robots (e.g., Riek et al., 2009). When people decided which of several robots they would save in the event of an earthquake, they overwhelmingly selected the robots that looked more human. Similarly, when participants viewed video clips of humans treating the robots unkindly (e.g., yelling at them), participants felt sorriest for the most humanlike robots, indicating moral concern. 

How Should Machines Make Moral Decisions? 

Even if people dislike machines making moral decisions, it is too late. Algorithms already decide who deserves an increase on their credit limit, who gets parole in pretrial decisions, and who should be triaged in medical contexts. They have also begun deciding who lives and dies in driving contexts. Much research on this topic has focused on self-driving cars or “autonomous vehicles” (AVs), and this area has become increasingly important given AVs’ involvement in deadly crashes. For example, in 2018, an Uber self-driving car in Arizona crashed into a woman bicycling across the street after failing to classify her as a pedestrian (Bevilacqua, 2018).  

As noted already, people generally do not want cars to make moral decisions (Bigman & Gray, 2018), particularly when human lives hang in the balance. At the same time, the rise of AVs means that such machines are forced to make some kinds of moral decisions only because of the timing. Imagine a human “driver” reading a newspaper in the backseat as an autonomous vehicle changes lanes, only to discover a group of children standing on the road. The car now has a fraction of a second to decide whether to swerve off the road and kill the driver or barrel ahead and kill the children. With far too little time for a human driver to be alerted and asked to decide, what is a car to do?  

One class of moral dilemmas studied in this context pits the lives of those in the car against the lives of those outside. When people respond to abstract dilemmas such as the one posed above (i.e., when people are not driving the car themselves), they generally want the car to make the “utilitarian” choice, saving the most lives possible in the situation (Bonnefon et al., 2016). So, if there are five children on the road and one person in the car, they want the car to swerve off the road, killing the passenger. However, people’s self-interest dominates responses when they consider themselves as the passengers inside the car. When they respond to these dilemmas as the driver, they want the car to preserve their own life (even at the expense of killing children). This makes sense—after all, who would want to be in a car that is willing to kill them? But, at the same time, who wants to live in a world where fleets of autonomous vehicles fail to protect the maximum number of lives? 

People care not only about the number of lives killed or saved but also about the identities of people they might kill or save. In the “moral machine experiment” (Awad et al., 2018), participants from around the world evaluated various moral dilemmas in which a car—not driven by them—could go straight and kill varying kinds of people crossing the road, or swerve and kill varying other kinds of people in the car. The key to understanding the moral machine experiment is that—by averaging across millions of people’s decisions — it extracts implicit preferences for different social categories. 

For example, people might have to decide between killing an older woman and a younger man, versus a female executive and a dog. Across all these dilemmas, people preferred to save humans (vs. animals), more people (vs. fewer people), the young (vs. the old), the lawful (vs. the unlawful), the rich (vs. the poor), the physically fit (vs. the less physically fit), and women (vs. men). People also preferred inaction (the car keeps going straight) versus action (the car swerves; Awad et al., 2018). One interpretation of this experiment is that it uses people’s decisions about how they want technology to behave to reveal biases about humans. For example, the study also revealed people have an implicit preference to save White people over Black people, consistent with people’s general implicit racial bias. But does this mean people want their car to be racist? 

A follow-up experiment to the moral machine experiment revealed that people—when given an explicit choice—generally do not want autonomous vehicles to discriminate based on sex, income, or other social categories when making life-or-death decisions (Bigman & Gray, 2018). People want autonomous vehicles to treat all people equally by ignoring social categories. However, people do want autonomous vehicles to be sensitive to structural features of situations, save more people rather than fewer, and prioritize inaction over action. This follow-up study revealed that while the moral machine experiment reveals people’s implicit preferences, one should not assume people want these preferences to guide policies or programming for cars. Just because people might be implicitly prejudiced does not mean they want to drive a prejudiced car. 

Beyond self-driving cars, many other machines, from military innovations to medical technologies, can make life-or-death decisions about humans. Although considerable research has examined how morality and ethics might be programmed into machines (Conitzer et al., 2017; Noothigattu et al., 2019; Petersen, 2020; Tolmeijer et al., 2021), it is for future work to examine how people want machines to make moral decisions.  

Consequences of Machines Making Moral Decisions 

Given the role that machines already play in making moral decisions, there is speculation and emerging evidence that these machines also alter how people subsequently make moral decisions. One theoretical review outlines two broad ways that machines could impact people’s ethics (Köbis et al., 2021): through setting an example and through enabling behavior. On the first point, machines can influence human moral decision-making by serving as role models. If an algorithm allocates scarce medical resources in a particularly utilitarian way (e.g., maximizing total lives saved rather than taking personal characteristics into account, like saving adults vs. children), people might oppose the decision in the abstract. However, if this algorithmic process becomes standard operating procedure, people’s own ethical intuitions might shift toward utilitarianism. 

Little existing work examines machines as ethical role models, but some insights come from work investigating conformity to robot behavior. As noted previously, a study by Qin et al. (2022) used a paradigm similar to Asch’s and found that robots are almost as effective at inducing social conformity as humans. Using a different paradigm, Salomons et al. (2018) observed that adults might also conform to robots if they trust them. In a task with no clear and objective right answer, some participants were allowed to view the answers of the robots around them before making their final call. Compared to participants with no such opportunity, these participants were more likely to change their final answers to match the robots’ answers. Other research reveals people are less likely to conform to robots than humans in an Asch-like paradigm (Brandstetter et al., 2014) and that adults (but not children) are resistant to the social pressure of robots (Vollmer et al., 2018).  

Still other work has shown that a key moderating factor of conformity to non-human agents is the type of task being performed, as is the case with general algorithm aversion and appreciation (Hertz & Wiese, 2018). Specifically, participants conformed to fellow humans, robots, and computers equally in analytical tasks (e.g., basic addition and subtraction) because they perceived all groups to excel at these tasks. However, people conformed to humans more than robots or computers on social tasks (e.g., judging emotional expressions), as they believed humans alone were skilled at such tasks. For social tasks in particular, conformity was higher when groups had more humans than robots (Hertz et al., 2019).  

Existing findings on conformity to machines are consistent with the basic premise that descriptive norms (what is typically done?; Monroe et al., 2018) influence people’s normative moral decisions (what should be done?). Currently, most moral norms are set by what other people do, but as people come to see machines as legitimate moral agents, machines too will influence how people make moral decisions. The exact ethical decisions that machines make are properties of their programming, giving the engineers and organizations behind these machines considerable moral responsibility. Technology companies and developers must understand that the moral choices they program in machines will affect not only the machines and their outputs but the humans who use and observe these machines as well. 

The second way that Köbis et al. (2021) suggest machines can influence human ethics is as enablers. Machines can theoretically enable humans to make unethical decisions by facilitating the diffusion of responsibility. That is, when humans use technology to make a moral decision, they can point to machines as partly responsible for making the decision, allowing humans to escape full responsibility. For example, some research suggests that using advanced technology, such as drones in warfare, could reduce the individual responsibility one feels toward harming others, enabling further violence (Bandura, 2011; Hijazi et al., 2019). In a context more broadly applicable to everyday technology users, Jackson and Williams (2019) argue that many language-capable robots are designed to clarify user requests, regardless of their moral impermissibility. For example, given an ambiguous and immoral command (e.g., “destroy a property”), robots are designed to seek clarification first (e.g., “which property should I destroy?”). Although many of these robots will ultimately reject the immoral command, the very step of clarification falsely signals a willingness of the robot to perform the immoral command, which can weaken human users’ moral standards within that context.  

Another way that machines can enable immoral behavior is by dulling moral outrage in response to discrimination. Moral outrage can catalyze social change, spurring collective action against societal inequities (Spring et al., 2018). However, as noted previously, people express less outrage toward algorithms that make sexist hiring decisions than humans that make similarly sexist decisions because they attribute less prejudiced motivation to the algorithm (Bigman et al., 2022). In work also previously described, people accept biased decisions toward women and racial minorities made by algorithms (Bonezzi & Ostinelli, 2021). Thus, the overall effect of algorithms making biased decisions of this type is that they can legitimize discrimination.  

Despite the possibility of machines as enablers of bad behavior, there is some optimism that acting through machines can improve ethical outcomes. At least one line of research shows that people make fairer decisions when interacting through an automated agent (i.e., an automated negotiator) than on their own (de Melo et al., 2018). Participants played economic games with ostensible partners and either made offers by themselves or through a computerized agent, exhibiting fairer offers in the latter case. This work suggests that interacting through an agent causes a person to think more carefully about the potential scenarios and outcomes they may face. In doing so, they expand their perspective to consider the other party’s position and follow more prosocial norms. Although machines might diminish feelings of responsibility, they can also alter one’s perspective and encourage less selfishness.  

THE THREAT OF REPLACEMENT AND PEOPLE’S REACTIONS  

When pundits discuss the rise of machines, they frequently wring their hands about the threat of replacement. They sketch bleak pictures of a future where robots take jobs, replace relationship partners, and fight wars. Science fiction movies present a similarly apocalyptic vision of the future. In Terminator, small bands of humans flee the cold onslaught of murder machines. In Deus Ex Machina, a beautiful but ruthless robot escapes from an underground bunker after outsmarting her cruel creator. In Blade Runner, people live in neon loneliness and fear the rebellion of robots they have enslaved. Although everyday people hold less dire visions of a machine-dominated future, the threat of replacement is ubiquitous, especially with the rise of Large Language Models (LLMs).  How well can LLMs replace people, and what are people’s reactions to replacement. 

Large Language Models: Replacing People and Human Participants  

The past few years have seen an unprecedented surge in the use of Large Language Models (LLMs), algorithms that generate human-like text with remarkable precision. The LLM platform ChatGPT set user growth records for obtaining 100 million active users just two months after its launch. LLMs are increasingly stepping into roles once held exclusively by people. Many people are relying on LLMs to write their emails, essays, and even scholarly papers (Bašić et al., 2023; AlAfnan et al., 2023; Conroy, 2023). But the use cases for LLMs have expanded well beyond writing to domains traditionally viewed as deeply human. People are turning to LLM applications for therapy (Stade et al., 2024). They seek companionship from LLMs, with some users forming romantic relationships with AI-generated partners (Depounti et al., 2023). Proposals have been put forth to leverage LLMs in educational roles, acting as tutors (Kasneci et al., 2023), and in healthcare, offering diagnoses and treatment guidance (Wilhelm et al., 2023). In the sciences, LLMs are helping to solve longstanding problems like protein folding and drug discovery (Lin et al., 2023; Chakraborty et al., 2023). 

Not only are LLMs taking on roles previously thought exclusive to people, they can also predict social behavior and beliefs with striking accuracy. In certain contexts, they may even show high enough correspondence with people to replace participants in psychological studies. In the realm of morality—a domain once considered beyond possible machine comprehension—LLMs have demonstrated a .95 correlation with average American moral judgments (see Figure 1; Dillion et al., 2023). LLMs show excellent theory of mind; GPT-4 solved nearly all of the tasks in a false-belief taskset traditionally used to test theory of mind in people, suggesting that LLMs can accurately gauge the implicit mental states of others (Kosinski, 2023). LLMs have been shown to use this theory of mind to beat people in debates (Salvi et al., 2024), and can write tailored persuasive arguments that outperform non-targeted ads (Simchon et al., 2024). The capability of LLMs to simulate participants extends to forecasting voting decisions, mirroring people’s heuristic reasoning, economic behavior, and problem-solving skills, and reproducing outcomes in classic social science experiments like the Ultimatum Game and the Milgram obedience study (Aher et al., 2023; Binz & Schulz, 2023; Horton, 2023; Argyle et al., 2022). LLMs can even predict the results of scientific experiments with greater accuracy than human experts (Luo et al., 2024). 

The wide range of social domains that LLMs can model suggests their potential to act as proxies in social science research (Dillion et al., 2023). However, current models are limited in that they better represent Western cultures than other cultures around the world, and display many of the prejudices implicit in their training data (Atari et al., 2023; Bender et al., 2021). With the advent of new models that incorporate globally diverse datasets and multilingual capabilities (e.g. Cohere for AI's Aya; Üstün et al., 2024), it is possible that LLMs may become more widely used to gain insights in the social sciences in coming years. 

Figure 5: The Relationship Between Human and GPT-3.5 Judgments

Note: The relationship between average U.S. participant judgments and GPT-3.5 (text-davinci-003)’s judgments on 464 moral scenarios from five published papers (r = .95; Dillion et al., 2023).  

Public perceptions of LLMs remain largely underexplored. Clearly many people use LLMs and find their capabilities valuable, but it is unclear to what extent they feel threatened by their potential as agents of replacement. One study examined the tone expressed in academic papers on LLMs and found neutrality to be the prevailing sentiment (Hong et al., 2023). Experts have expressed concern about the mass integration of LLMs in public life, including their potential to perpetuate the biases embedded in their training data, and the fact that they may replace several jobs (Eloundou et al., 2023; Bender et al., 2021). One analysis found that up to 15% of job tasks in the U.S. could be automated by LLMs, and this estimate was higher when considering additional software that can be built in conjunction with LLMs (Eloundou et al., 2023). It remains to be seen how society will adapt to the expanding capabilities of LLMs, but one thing seems clear: people do not appreciate the specter of replacement. 

The Threat of Replacement  

Being replaced, whether in a relationship or a job, makes people feel devalued and evokes threat—feelings of discomfort, anxiety, and fear. Importantly, as with other psychological phenomena, threat is a matter of perception, which means that people can feel threatened even if there is little objective basis for this threat.  

Integrated threat theory outlines two different kinds of perceived threats: realistic threat and symbolic threat (Stephan & Stephan, 2000). People view realistic threats as endangering the group’s continued existence and ability to protect itself, harming physical and economic well-being or political power (Campbell, 1966). Realistic threats can include the threat of genocide, political disenfranchisement, and economic subordination. Symbolic threats are more abstract and people see them as endangering the group’s identity, especially by attacking cherished values or morals. Symbolic threats include being banned from openly practicing religion or wearing culturally important clothes.  

This theory is also instructive for understanding how people react to the rise of machines because it speaks to situations where people perceive other groups as attempting to replace them. It is up to debate who or what poses an actual threat to human safety, economic well-being, and our moral values. But people clearly feel threatened by the specter of replacement by machines, and it is important to understand the consequences of these feelings.  

Reactions to Replacement by Machines 

Whether accurate or not, people fear being replaced by machines, especially in the workforce. The Chapman Survey of American Fears in 2015 found that about 30% of respondents reported concern with robots replacing the workforce (McClure, 2018). These results were replicated by Morikawa (2017), who found 30% of workers surveyed were afraid of their jobs being replaced by AI and robotics—a high number given that robots are likely not replacing many people and, in some cases, have increased employment.   

Evidence is mixed on the impact of the rise of machines on employment. One study finds that robot adoption in Spanish manufacturing firms increased net jobs by 10% (Koch et al., 2021). However, other work finds that one more robot per thousand workers reduces the employment-to-population ratio by about 0.2 percentage points and wages by 0.37% within commuting zones (Acemoglu & Restrepo, 2020). Other studies have found that robot adoption does not lower overall employment—rather, robot adoption does lower low-skilled employment (Graetz & Michaels, 2018) and manufacturing jobs, but increases business-service jobs (Blanas et al., 2019; Dauth et al., 2018). 

However, even if the actual impact of machines on employment is unclear, people across industries feel threatened by machines (e.g., Lingmont & Alexiou, 2020), from marketing/sales providers (Wirtz et al., 2018) to healthcare providers (Reeder & Lee, 2022). Yam and colleagues (2022) found this perceived threat is prevalent across industries, jobs, and cultures.  

Spurred on by these feelings of threat, workers have spearheaded protests. In 2018, 50,000 Las Vegas food service workers went on strike to gain protection from their jobs becoming automated (Nemo, 2018). Dockworkers in California have also protested the scope of automation in their jobs (Hsu, 2022). French supermarket workers have protested automation by blocking doors and tipping over shopping carts (Iza World of Labor, 2019), and truckers in Missouri have organized to protest self-driving trucks (Robitzski, 2019). People’s concern about being replaced by machines appears to extend across countries (Wike & Stokes, 2018), with only mixed evidence of any meaningful cultural differences in this concern (Bartneck et al., 2005; Dang & Liu, 2021; Gnambs & Appel, 2019).   

Fortunately, at least one positive outcome has emerged from the perceived threat of machines—it can spur workers to learn new skills. A representative survey across 16 countries found that workers with more fear of automation reported greater intentions to seek training outside their workplace (Innocenti & Golin, 2022). Workers also generally advocate for more training opportunities to protect their jobs against automation (Di Tella & Rodrik, 2020). Perhaps ironically, Tang et al. (2022) found that employees who work alongside machines were rated by their leaders as being more productive.  

Of course, not all machine replacement scenarios are equally threatening. In repeated interactions with a robot, one may find the robot useful in one interaction and feel threatened in the next (Paetzel et al., 2020). Initial threat perceptions toward machines may also dampen over time as people come to build trust with them (Correia et al., 2016). Additionally, social influence can increase the acceptance of robot replacement. People are less threatened by robots when they (the people) are in a group (Gockley et al., 2006; Michalowski et al., 2006) and are more accepting of healthcare robots in particular when their peers support using these robots (Alaiad & Zhou, 2013).  

Much variation in people’s perceived threat of robot replacement can, again, be explained by integrated threat theory, which suggests this anxiety increases when realistic threat is high (people believe machines are taking material resources such as jobs or wages) or symbolic threat is high (people believe that robots are negatively affecting their values or identity). In some cases, both threats occur simultaneously, such as in work showing that people perceive humanlike robots who can outperform humans as threatening their economic well-being and their human identity, thus reducing support for robotics research (Yogeeswaran et al., 2016). Other work has shown that hotel employees who have recently started working alongside robots experienced increased feelings of both symbolic and realistic threats when they perceived robots to have greater advantages over humans at the job (Lan et al., 2022). In many cases, however, one form of threat predominates.  

Realistic Threat  

Realistic threat seems to explain the scattered findings on how demographic and job characteristics interact with this aversion. Overall, this emerging literature demonstrates that people are anxious about robot replacement to the extent they feel that their jobs and earnings might be threatened. More job-insecure individuals, such as older people, lower-income people, and members of racial minority groups, were more likely to see their jobs threatened by automation compared to younger people, higher-income people, and members of racial majority groups (Ghimire et al., 2020). Similarly, in organizational settings, top managers (who have relatively secure jobs) are more enthusiastic about promoting the use of machines, whereas middle managers and frontline employees are far more skeptical (Kolbjørnsrud et al., 2017). 

Other work shows that people whose jobs involve a high degree of social interaction (e.g., sales) report less machine-induced anxieties. This is likely because they feel that these socio-emotional jobs are less at risk of being done by machines (Coupé, 2019). Similarly, women appear less threatened by machines (Gallimore et al., 2019), partly because they tend to occupy jobs involving more socioemotional skills that people view as more “robot-proof.”  

In further support of realistic threat being a moderator of robot anxiety, people seem less concerned about robots fulfilling jobs that need filling (Enz et al., 2011). That is, robots appear less threatening for jobs with high demand (e.g., health care; Beran et al., 2015) or jobs that appear too risky for humans to perform (e.g., cleaning up nuclear disasters). The perceived threat of robots taking jobs will likely not occur when there are too few people to do these jobs or people simply do not want to do them.  

Symbolic Threat 

Even when one does not feel like his livelihood is threatened by robots, people might experience symbolic threat from the emergence of automation. One common circumstance whereby robots evoke symbolic threat is when they demonstrate superiority to humans or proficiency in a domain that seems special to humans. The world takes a collective gasp every time a machine beats a grandmaster at a game like chess or Go, which once seemed to require uniquely human cognition.   

As noted already, people are more threatened by robots taking socioemotional jobs that require feeling more than thinking (Waytz & Norton, 2014), underscoring the belief that experience (i.e., emotion) is a distinctively human and lacking in machines (Gray & Wegner, 2011). This aversion to machines doing socio-emotional jobs represents a symbolic threat of losing something once considered unique to the human identity.  

Despite some acceptance of anthropomorphic robots, studies also show that people perceive highly humanlike robots as threatening human identity (Vanman & Kappas, 2019; Złotowski et al., 2017). These findings on threat to identity may also explain why people dislike the idea of an anthropomorphic robot boss, particularly when it delivers negative feedback (Yam, Goh et al., 2022). 

Although considerable research demonstrates people’s anxiety regarding machines replacing humans generally, other work reveals that people themselves would prefer to be replaced by a machine than by another human. A set of studies found that participants as observers preferred to replace a human at a job with another human rather than with a robot. However, when participants took the employee's perspective about having their job replaced, they preferred being replaced by a robot over a human (Granulo et al., 2019). These studies suggest this reversal of aversion occurred because robots are commonly perceived as “infallible” entities, thus, eliciting less identity-relevant social comparisons than fellow humans. In other words, getting replaced by a robot does not threaten a person’s self-identity as much as getting replaced by another person does (Tesser et al., 1988). Even though these results differ somewhat from other studies on robot replacement, they all align with the common idea that people generally dislike being replaced, especially when it symbolically threatens their identities.   

Replacement Beyond the Workplace: Art, Sex, and God  

Beyond taking on jobs that people feel are linked to their livelihoods and identities, machines are entering other spheres that people consider essentially human, such as artistic creativity. In the quest to define what makes humans special, many would say the defining attribute is creativity. Animals are smart—dolphins can engage in self-recognition (Reiss & Marino, 2001), crows can solve puzzles (Taylor et al., 2010), and dogs can read human emotions (Müller et al., 2015)—but only humans have the creativity to write novels, craft symphonies, and make inspiring paintings (Fuentes, 2017). Machines may lack “authentic” creativity, but they can create compelling art, which has begun to spur replacement threat similar to the threat evoked by job automation generally. The AI image generation tool Midjourney makes pictures so good they can win art competitions (Ghosh & Fossas, 2022) and be published in national magazines (Figure 6). 

Figure 6: Théâtre D’opéra Spatial

Note: This AI-generated work, “Théâtre D’opéra Spatial,” won the 2022 Colorado State Fair’s annual art competition. Generated with Midjourney. Copyright 2022 by Jason Allen. Reprinted with permission. 

 Controversy ensued over a writer at The Atlantic using such technology to create an illustration for an article, with artists protesting the use of AI instead of a paid designer to create the image (Naughton, 2022). Artists are also threatened by AI’s increasing role in generating comics (Martens & Cardona-Rivera, 2016) and film (Hong et al., 2021), and anyone who cares about humans’ ability to perceive reality accurately is threatened by AI “deepfake” videos that portray perfect replicas of real people saying outlandish things (Lyu, 2020). 

AI has also become adept at writing like a human. The language model GPT-3 (Generative Pretrained Transformer 3) can mimic the styles of famous writers and generate new works by them on demand (Elkins & Chun, 2020). Although the quality of GPT-3 output varies in accuracy and legibility, its best writing is indistinguishable from that of a human. It can create plots, make jokes, write poetry, and reflect the wealth of knowledge available from its huge set of training data (Elkins & Chun, 2020). It has co-authored a law review article titled, Why humans will always be better lawyers, drivers, CEOs, presidents, and law professors than artificial intelligence and robots can ever hope to be (Alarie, Cockfield, & GPT-3, 2021), and its writing can sometimes surpass that of a typical college student (Elkins & Chun, 2020). Educators are concerned that GPT-3 is so proficient at writing it could enable students to cheat on tests (Dehouche, 2021). Reddit message boards reveal examples of students using the program to write essays (e.g., Reddit, 2020).  

ChatGPT has unparalleled power to mimic human writing and raises many important questions. How exactly should we think of the contributions of ChatGPT? Like a fancy dictionary, an inspiring muse, or a separate complete intelligence? Out of all the AIs reviewed here, ChatGPT seems to have the most concrete potential for replacing white-collar jobs, but the scientific study of ChatGPT is only just beginning, so we leave its review to some later chapter. 

Beyond writing realistic text, AI programs can even produce original songs and may one day teach music lessons (Zulić, 2019). People generally perceive AI-generated music and other artwork as lower quality than human-made artwork (Boden, 2007; Ragot et al., 2020; Wilbanks et al., under review). This is partly because people believe AI-generated art lacks emotional expression and uniqueness (Boden, 2007). People enjoy art that connects them to the artist's mind, and the lack of perceived mind in AI also leads people to view AI-generated art as inauthentic and incapable of reflecting true experience (Wilbanks et al., under review). However, people who are already accepting of AI creativity evaluate AI music more positively (Hong et al., 2021), and if AI-generated art continues to expand into the mainstream, people may soon listen to and enjoy pop music generated solely by machines.  

One of the most essentially-human tasks is physical intimacy, and yet, robotic sex dolls have begun to enter this realm, with some even designed to sense human emotions (Belk, 2022). Some seem to perceive robot lovers as equivalent to human lovers as well, with one survey revealing 42% of men and 52.7% of women believe that sex with a robot would be cheating (Brandon et al., 2022). In another study, women were asked to describe their reactions to their partner having sex with a human woman versus a robot, and they reported equivalent scores on some dimensions of jealousy between the two scenarios (Szczuka & Krämer, 2018). More research is needed to understand everyday people’s feelings about sex with machines, but theoretical discussion has already begun to examine the relationship between machine intimacy and slavery, prostitution, autonomy, and human agency (Devlin, 2015; Richardson, 2016).  

As strange as it is to think about machines fulfilling carnal human urges, many might find it even stranger that machines could replace another essentially-human role—spiritual leaders. In most religions, humans hold a special place in God’s cosmic order, especially humans who serve God in leadership roles as priests, pastors, rabbis, imams, and nuns. It may seem hard to imagine a machine filling this role, but already Mindar, a robotic priest, has taken over the job of giving sermons in one of the largest temples in Japan. A preliminary study conducted in this very temple (Jackson, Yam, Tang, Liu, & Shariff, under review) shows that although most visitors liked Mindar, they also donated less to the temple (a custom for temple visitors in Japan) compared visitors who observed a human priest was giving sermons. Interestingly, those with the strongest religious conviction were unaffected by such exposure, suggesting that religiosity might buffer negative responses to robotic religious leaders.    

Social Consequences of Replacement  

The most obvious consequence of robot replacement is that it produces feelings of threat, but how does this threat affect the fabric of society?  There are three possibilities. The first possibility is that this threat changes very little. Social robots and artificial intelligence are far from the first technologies to induce feelings of threat, with cars, the printing press, and even recorded music all predicted to produce society's downfall. John Philip Sousa (1906) railed against  “the menace of mechanical music,” stating, “I foresee a marked deterioration in American music and musical taste, an interruption in the musical development of the country, and a host of other injuries to music in its artistic manifestations” (p. 278). Of course, these alarmist predictions typically do not manifest, as people adapt to technologies that become commonplace. Recorded music did not destroy music in America.  

The second possibility is that the rise of robots will tear society apart. Unlike previous technology panics, social robots’ ability to replace humans means the threat they pose to material resources (e.g., jobs) and to core values around work could activate not only disdain toward robots as a social group (as suggested by integrated threat theory; Stephan & Stephan, 2000), but also toward other groups seen to pose similar threats. Some preliminary evidence for this pattern comes from work showing that exposure to automation fosters negative sentiment toward immigrants. Such exposure makes people feel that this group (immigrants) threatens realistic resources and symbolic values in a way that robots might (Gamez-Djokic & Waytz, 2020). Other work has found that people at greater risk of having their jobs replaced by automation oppose immigration at higher rates (Wu, 2022a), and that automation threat makes people support policies that restrict immigration and foreign goods (Wu, 2022b). In general, it does appear that people exposed to automation and machines are more likely to be disengaged from their work and engage in social undermining toward their colleagues, preassembly to safeguard themselves from unemployment (Yam, Tang, et al., 2022). These studies show how robot replacement could harm intergroup relations and increase prejudice toward marginalized groups.  

A third possibility is more optimistic: the rise of robots could bring about greater cooperation between groups. This possibility rests on the idea that robots function as a threat to all humans, which therefore facilitates the recategorization of any two potentially antagonistic human groups into one that shares a common identity (humans) as predicted by the common ingroup identity model (Gaertner et al., 1993). Support for this possibility comes from work showing that robots could reduce prejudice by highlighting commonalities between all humans (Jackson et al., 2020). This work found that anxiety about the rising robot workforce predicted less anxiety about human outgroups, and priming the salience of a robot workforce reduced prejudice and increased acceptance toward outgroups. In an economic simulation, the presence of robots reduced racial discrimination in wages. This work also suggests that as robot workers become more salient, human intergroup differences—including racial and religious differences—may seem less important, fostering a perception of a common human identity (i.e., “panhumanism”).  

More work is needed to understand how people react to the possibility of replacement by machines, but existing work demonstrates consistent feelings of threat. Even if machines are not actually a threat to us, these feelings of threat might spell trouble for the fabric of society…or perhaps not. People may adapt to the growing roles of machines, and time will tell what the future of human-machine coexistence has in store. 

FUTURE DIRECTIONS 

Machines are ever advancing, but so is research on machines, and many fruitful areas of research are expected to emerge in the years to come, several of which are suggested here. The first suggested area of future research is simply more collaborations between psychologists and technologists (programmers, computer scientists, engineers, and roboticists). The goal of technologists is often to enhance efficiency and effectiveness. The team of engineers and developers behind Google Maps, for example, seeks first and foremost to provide accurate location information that enables tourists to travel easier, small businesses to thrive, and people to navigate quickly in crises (e.g., to find a hospital; Life at Google, 2021). These goals are admirable, and they have made Google Maps a valuable tool. Psychologists also try to help people but are often focused on more fundamental questions of understanding how people think about technology, what they expect from technology, how they use technology, and what the social effects of technology are. Although today these issues tend to be lumped under the domain of “user experience,” this is where a more formal social-psychological approach can be extremely valuable.  

Second, although much of this chapter explored people’s reactions to machines replacing humans broadly, an additional vital area to study is how novel technology has supplanted the human mind. The philosopher Andy Clark argues that technologies like robots and AI represent part of the “extended mind” to which people outsource cognition, just like using a shopping list to remember what to get at the grocery store or a calculator to do math (Clark & Chalmers, 1998). As an example of AI functioning as part of the extended mind, when people use Google to search for information, they fail to distinguish between knowledge stored in their own memories and knowledge stored on the internet (Ward, 2021).  

Other studies have shown that merely searching for information on the internet leads people to feel like they understand this information better (Fisher, Goddu, & Keil, 2015), as though the technology has supplanted their personal cognitive capacity. This work builds on initial research (Sparrow, Liu, & Wegner, 2011) demonstrating that when people expect to have access to Google, they exhibit poorer memory recall, ostensibly because they have outsourced their memory to the search engine. Although the reliability of this particular effect has been questioned (Camerer et al., 2018), it is certainly true that the rise of machines has generally altered how much we consider our cognition to reside solely within our minds.  

One could argue that machines are not merely replacing our cognitive capacities but augmenting them, improving human cognition. A compelling area for future research is on transhumanism, more broadly, how technology might enhance aspects of people’s physical and mental selves. Questions for social psychology to answer include not only whether such augmentation is possible as well as whether it is merely “perceived” (resulting from misperceptions, as in the case of people “mistaking” the internet’s knowledge for their own).  

Future work should also study people’s views on whether machine augmentation is morally acceptable. People morally oppose strength-enhancement drugs (Landy, Walco, & Bartels, 2017) and cognitive-enhancement drugs (Fitz, Nadler, Manogaran, Chong, & Reiner, 2014; Scheske & Schnall, 2012), which means that they might similarly view technological augmentation as morally wrong or harmful (Schein & Gray, 2018). Some work has suggested that people morally approve of neurotechnological treatments that alleviate deficits or illness, but morally disapprove of neurotechnological treatments that enhance people’s cognitive capacities to superhuman levels (Koverola et al., 2022). In addition, given that people oppose technologies that play a role in processes related to human life and death (Waytz & Young, 2019), they might apply similar moral opposition to technology that replaces other human functions as well. 

A related area for psychologists to examine is how exposure to automated agents produces psychological and societal changes. As machines replace human capabilities—for example, an app that gives us directions rather than a human whom we stop on the street to ask for directions—there are likely significant downstream effects. Given that interacting with others through machines may decrease sociability (i.e., capacities for emotion recognition, empathy, perspective taking, and emotional intelligence; Waytz & Gray, 2018), interacting with machines instead of people will affect our social abilities even more potently.  

The rise of machines is likely to affect political behavior and cognition as well, as studies have begun to show. The spread of automation can influence support for far-right political parties in both the United States and Europe (Anelli, Colantone, & Stanig, 2021; Dal Bó, Finan, Folke, Persson, & Rickne, 2018; Frey, Berger, & Chen, 2018; Zhen et al., 2017) in part because the experience of automation as a threat shifts people’s political leanings toward conservatism. Other work on the societal impact of robotics has examined how exposure to automation affects support for redistributive economic policies, such as a universal basic income—this work has found mixed effects (Busemeyer & Sahm, 2021; Gallego, Kuo, Alberto, & Manzanos, 2022; Kurer & Hausermann, 2021; Thewissen & Rueda, 2019), suggesting a ripe area for future exploration.  

Beyond politics, other work has begun examining how artificial intelligence and robots are replacing not only human beings but are also taking on some of the properties of Gods, thereby reducing the global importance of religion (Jackson, Yam, Tang, Sibley, & Waytz, under review). If our gaps in knowledge are being filled by machines, then God is ousted from the gaps. Perhaps one day people will predominantly view machines as divine creations, as mechanical emissaries of God. Once machines have intelligence that humans cannot fathom, it is not such a big leap to psychologically associate them with God. 

Finally, one essential area of future research is understanding whether all the excitement, anxiety, and novelty examined in this chapter might become quaint in just a few years’ time. As social robots and artificial intelligence become increasingly integrated into our lives, might their overall psychological effect on humans shift or wane? Consider the uncanny valley. Adults and older children, but not younger kids, find machines that exhibit feelings creepy, suggesting that the uncanny valley is learned (Brink, Gray, & Wellman, 2019). This means that this phenomenon could also be unlearned. Gopnik (2019) raises the possibility that the uncanny valley phenomenon could cease to exist as future generations become more exposed to smart technology and thus more comfortable with technology that appears mentally capable.  

As people grow more accustomed to advanced technology, its mere existence will likely engender more positive feelings (Eidelman, Crandall, & Pattershall, 2009). Already, research has shown that simply describing technology as originating before (vs. after) one’s birth makes people evaluate it more favorably because, apparently longstanding technology feels more like the status quo, toward which people are positively biased (Smiley & Fisher, 2022). At some point in the future, perhaps soon, social robots and artificial intelligence —like the car, the telephone, and the personal computer— might become so common and embedded in our lives that rather than having positive or negative effects, these technologies will have little psychological effect at all. If people entirely habituate to machines, then within a generation, this entire chapter could be obsolete, just like the electromechanical calculator and tape player.  

CONCLUSION 

How do humans make sense of machines? In general, people understand our social world through agent-based cognition, categorizing entities based on the kinds of minds they seem to have. Machines represent a special kind of agent, an agent of replacement, explicitly designed by people to replace other agents—typically people. The goal of some of the earliest intelligent machines was to replace human minds, and modern machines are getting ever closer to this goal. Because machines are agents of replacement, they create a fundamental ambiguity about whether people should think of them more as just a machine, or as the human role they are replacing—especially when their appearance and behavior place them within the questionable zone (QZ) within a continuum ranging from “simple machine” to "complete human replacement.” 

Modern machines can serve as coworkers, teammates, nurses, and restaurant servers. On the one hand, these machines can make our lives easier and more efficient, but people are not always excited about machines replacing people. Sometimes, they are downright averse (or creeped out) by machines that replace human minds and jobs, especially when it comes to involving moral decisions. To the extent machines do decide on moral matters, people want them to be as fair and impartial as possible.  

The rise of machines will likely change the fabric of society and alter what we think of art, sex, and maybe even God. One day, machines might even replace scientists. Perhaps the next edition of The Handbook of Social Psychology will be written entirely by an algorithm.  

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ENDNOTES

The Handbook of Social Psychology, 6th edition © 2025 by Situational Press is licensed under Creative Commons- Attribution-NonCommercial-NoDerivatives 4.0 International. This work may be copied and distributed only in unmodified form, for noncommercial purposes, and with attribution.

We use this term given its canonical status, but note it has since come to represent aversion toward not only algorithms but related technologies that may or may not rely on algorithms per se. 

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