Social technologies have profoundly transformed how people communicate, form relationships, and experience social connection. This chapter synthesizes two decades of research on social media and messaging platforms to extract lessons for new forms of social technology, such as empathic artificial intelligence. We propose that prior attempts to identify universal effects of social technology on connection are largely uninformative due to substantial heterogeneity across studies. We highlight and briefly review three key sources of heterogeneity: (1) the type and features of the technology, (2) how it is used, and (3) who is using it. We recommend that future research explicitly consider these sources of heterogeneity to better understand how technological advances will affect social life – and how they can be better designed to promote connection.

Efforts to develop “empathic AI” often assume a universal definition for empathy – a singular, objective understanding of “human” experiences. This framing treats achieving empathic AI as an optimization challenge, where researchers should find the “correct” way to align AI with human empathy. In this chapter, we challenge this assumption by emphasizing the empathy’s multifaceted nature and examining how variation across its forms and the social groups we are embedded within complicates efforts to design a universally applicable empathic AI. We argue that codifying empathy into artificial systems is difficult, given that empathy is an inherently context-dependent phenomenon. Drawing on recent evidence of cultural biases in large language models, we highlight broader ethical, epistemic, and potentially existential concerns in designing machines that “understand” or “feel” others. We conclude by advocating for a more contextualized and culture-aware approach to empathic AI, moving beyond the reductionist notion of “humans” as a monolith.

Empathizing with another is perhaps the most human emotion of all and a long shot from the cold calculations of vectorized tokens that drive the inner workings of contemporary LLMs. This chapter looks beyond the questions of what to make of AI-generated “empathy” and instead asks whether AI can be used to develop our capacities for genuine human empathy. Empathy is not a static trait, instead capable of growth and development, and this paper explores whether AI can and, more importantly, should be used to increase one’s empathy. Nothing in principle stands in the way of AI improving our empathy, but the possibility raises unanswered questions about whether such an approach would be effective or would backfire in unexpected ways, such as encouraging the commodification of empathy as a technological tool that can make companies money, rather than a fundamental part of the human experience.

Most definitions of empathy stress its interpersonal nature: Empathy requires a human sender and a human receiver, where all parties involved have the theoretical capacity to accurately understand each other’s minds. Because empathy is inherently interpersonal, some scholars have questioned whether empathy between humans and artificial intelligence as it currently exists (e.g., large language models) is “real.” However, the requirement for two minds, each with the theoretical capacity to fully understand the thoughts and emotions of the other, is also not clearly met in other domains where empathy is discussed, such as empathy between humans and non-human animals or empathy with nature. This chapter critically examines these assumptions about what is needed for empathy to occur, offering several possible sets of empathy requirements that researchers might engage with, and outlining potential challenges researchers might face if they adopt any particular set of requirements in their work. Ultimately, we pose the question of whether two minds and/or the ability for minds to understand each other are truly necessary for empathy, or whether all that is required for empathy to occur is an entity’s perception of giving or receiving it.

Our chapter will discuss the role that trust and social affiliation play in human evacuee decisions to continue following a robot that produces questionable guidance directions during an emergency. Our research has demonstrated that people will follow a robot’s guidance directions during an emergency even if the robot has demonstrated its fallibility or the robot’s directions are illogical. We have recently completed extensive experimentation using a combination of virtual reality and physical experiments. Our results suggest that social affiliation may be a driver of decisions to follow the robot in spite of clear evidence that doing so will put the person in harm’s way. The findings suggest that increasing a robot’s ability to interact with evacuees may foster empathy with the robot, increasing risk for the evacuee in contexts where the robot is fallible or where the best decision is to abandon the robot.

Recent advances in large language models (LLMs) have enabled artificial systems to generate responses widely perceived as empathic, raising fundamental questions about the nature and value of empathy. This chapter examines why, despite evidence that AI can equal or even surpass humans in tasks of cognitive empathy, human empathy remains uniquely valued by recipients. We review empirical findings comparing human and AI empathic responses, highlighting the critical role of perceived source, effort, motivation, and trust in shaping recipients’ experiences. We argue that the value of empathy depends on the type of empathy sought – cognitive, affective, or motivational – and that AI may be well suited for some empathic functions but not others. Finally, we consider the broader societal implications of artificial empathy, including cultural variation, trust, skill erosion, and the future of human relationships, outlining key open questions for research on empathy in an AI-mediated world.

The notion of “AI empathy” has been criticized on the grounds that AI lacks the capacity to authentically share in the experience of the empathy recipient, or to be selective in its choice to empathize. In this chapter, we challenge the argument that human empathy is inherently or universally superior to AI empathy and suggest that a more fruitful way to frame this debate is in terms of the possible trade-offs between what human and AI sources of empathy can offer in particular situations. Emphasizing the empathy recipient’s perspective, we explore possible dimensions in which each empathizer source may excel, shaping the recipient’s possible preference for one source over the other. Drawing upon different ethical frameworks, we then discuss the possible normative consequences of whether one ought to accept or avoid AI empathy, given these trade-offs.

A wealth of studies suggest that children anthropomorphize artificial intelligence (e.g., voice assistants, humanoid robots) to a greater degree than do adults, suggesting that children may recognize at least some forms of artificial intelligence as meaningful "social" partners. Adults, however, seem more divided on this premise. For example, adults sometimes admit to feeling "more heard" by AI-generated empathic responses than human-generated empathic responses – but only when they believe the empathy came from a human. Would children similarly reject "artificial empathy"? In this chapter, I review developmental evidence that suggests otherwise: Today’s children (and future children) may very well accept artificial empathy as on par with empathy from humans. This may even come to affect how future generations value empathy in human-human interaction.

We may soon be interacting with AIs that simulate empathy and similar capacities well enough to be indistinguishable from, or even superior to, human companions. This chapter discusses the costs and benefits of such interactions. While there are substantial benefits, I conclude that it is nonetheless better to interact with real individuals than simulated ones—there is an intrinsic value to real human connection.

Artificial agents sometimes elicit surprisingly strong emotional responses, ranging from care and sympathy to discomfort or moral concern, for example in reactions to the mistreatment of robots. This chapter asks why such reactions arise at all and how humans come to engage empathically with artificial agents. We address this question by conceptualizing empathy not as a purely inferential process, but as an embodied and relational phenomenon grounded in bodily awareness, intersubjectivity, and social presence. Drawing on psychological, neuroscientific, and developmental research, we suggest that physical embodiment plays a critical role in scaffolding empathic responses, making embodied agents more likely to be engaged with as socially significant than virtual or disembodied counterparts. This perspective also allows us to examine what embodiment contributes to social interaction more broadly. We conclude by considering the practical and ethical implications of empathic engagement with artificial agents for social life, collaboration, and care.

Advances in the performance of large language models (LLMs) have led some researchers to propose the emergence of theory of mind (ToM) in artificial intelligence (AI). LLMs can attribute beliefs, desires, intentions, and emotions. But, rather than employing the characteristically human method of empathy, they learn to attribute mental states by recognizing behavioral and linguistic patterns in a dataset. We ask whether LLMs’ inability to empathize precludes them from honoring an individual’s right to be an exception, from assessing character with appropriate sensitivity to a person’s individuality. We defend the following claims: (1) Both humans and LLMs can honor the right to be an exception. (2) Humans can honor that right through empathy, and LLMs cannot empathize. (3) There is distinctive moral value in honoring the right to be an exception through empathy.

Perceiving humans is distinct from perceiving AI due to a difference in impression management concerns, the engagement of a moral boundary, and a lack of physiological and behavioural synchronicity, borne out in distinct patterns of brain activity when people encounter AI. Empathy results from predictive and mental inference processes that inform and promote emotion resonance, motivating behaviour. The distinctions just described suggest a difference in the capacity of human beings to experience empathy toward AI. Here, I discuss how each distinction affects the potential experience of empathy by human beings toward AI. I explore the extent to which impression management concerns suggest differences in the quality of mentalizing with humans compared to AI as interaction partners. I focus on how logical reasoning processes are differentially engaged when considering AI as moral agents, affecting empathy as a downstream response to AI experiences. Finally, I focus on how a lack of the capacity for synchronicity may block empathic responses to AI and discuss ways AI must behave, and psychological processes humans must engage in to experience empathy toward AI.

Psychological theories of empathy have traditionally focused on the empathizer’s ability to perspective take and share another’s emotions. But people today live in a day and age where AI models, like Large Language Models (LLMs) devoid of emotions and other empathic capabilities, can still produce text that makes people feel heard and comforted. Surely what people perceive in these artificially-generated responses still amounts to some form of “empathy”? To better understand this, we motivate a focus on received empathy. How empathy is received is impacted by many factors, such as recipients’ concern about maintaining self-esteem, that are nevertheless not considered by traditional psychological theories of empathy. Most importantly, the existence of "empathy" expressed by these artificial agents – what we call "LLMpathy" – allows us to shift the discussion away from givers of empathy toward empathy as perceived by the recipient. We consider the implications of receiving LLMpathy from these agents.

In this chapter, I bring attention to how affectively loaded conversations are grounds for interpersonal inquiry and function as sounding boards for self-interpretations. In particular, I argue that receiving empathy often functions as a form of validation of our stories, and that AI-simulated empathy can constitute feedback integrated in our self-narration. Relying on narrative views of self-understanding and on the idea that our self-narratives are socially situated, I claim that technologies meant to simulate human conversation and interactions can potentially change the socio-cultural conditions of our self-narration practices, and that this deserves further scrutiny.

AI developers are beginning to claim that their products have empathy and that this improves their ability to succeed at various tasks, including medical advice and psychotherapy. This chapter argues that AIs can succeed in these practical tasks just as well without real cognitive empathy, affective empathy, or compassion, but they still need an appearance of empathy and motivation to help.

A question that arises when contemplating artificial intelligence and empathy is whether it would matter to the empathy recipient if AI genuinely feels or cares. This is a seemingly novel issue, born of the capacity of computers to simulate human empathy. I will suggest, however, that questions concerning “artificial empathy” should not be limited to AI chatbots, as such forms of empathy that are void of feeling are widespread in human relationships as well. This chapter offers a short history of artificial empathy and how it became widely accepted, well before AI, beginning with the operationalization of empathy in mid-twentieth-century clinical psychology. Evaluations of AI’s empathetic abilities are often based on comparisons to human or “real” empathy – a romanticized form of empathy that is, sadly, less common than we appreciate. This chapter offers an alternative perspective that might prove valuable, comparing AI’s “artificial empathy” to human “artificial empathy.”

Human kindness, especially in relationships, revolves around exchanging empathy. Empathic AI – large language models (LLMs) that provide empathic support – will undermine human kindness by stopping people from (1) seeking empathy and (2) giving empathy. Asking humans for empathy is often an imposition and can be expensive (e.g., therapy). Also, the empathy people receive from humans is often unwelcome advice (rather than pure sympathy). Because LLMs are always available to provide consistent and unconditional compassion, “seekers” will rely on LLMs instead of their relationship partners. And, when seekers rely on AI, “givers” will happily offload their empathic responsibilities to save themselves from this uncomfortable and emotionally costly work. With seekers and givers both relying on LLMs, the kindness created by exchanging empathy will disappear – unless humanity uses empathic AI only to reinforce human empathy, not replace it. The future of human kindness hinges on how we choose to use LLMs.

Artificial Intelligence systems increasingly match or surpass human performance across a wide range of cognitive tasks. Recent advances in large language models have yielded conversational abilities often indistinguishable from human dialogue. In this chapter, we argue that whether artificial agents should be considered empathetic depends on how empathy itself is defined and how it relates to subjective experience. We show that different philosophical schools produce distinct, and often incompatible, answers to the question of whether such a system should be considered genuinely empathetic. On one hand, empathy is a functional trait that can be fully characterized by observable behavior; on the other hand, empathy is inseparable from subjective experience and conscious feeling. Drawing on the philosophical notion of the “zombie” we introduce a thought experiment involving a hypothetical chatbot that exhibits perfect empathic behavior across all conceivable benchmarks while lacking any subjective experience. This framing allows us to disentangle functional performance from conceptual attribution and to examine whether empathy judgments depend on observable behavior alone or on assumptions about inner experience. As artificial agents become increasingly integrated into emotionally contexts, understanding how and why humans attribute empathy to machines becomes a pressing scientific, ethical, and societal question.

This chapter explores some conceptual connections between human-centered artificial intelligence (HCAI) research and empathic AI. First, I argue that HCAI is best understood as a framework that centers human well-being in AI development and deployment. Second, I argue a fruitful way to make the framework more precise is by focusing on human practical capacities. I use empathy as a case study, examining recent positive accounts of empathic AI from the perspective of a well-being-focused HCAI. Finally, I note how this approach relates to recent calls for a sociotechnical perspective on AI.

AI, with its strong emotion recognition and empathic response generation capabilities, has the potential to dramatically impact how humans receive and provide empathy. However, as AI assumes a more significant role in empathy provision, important questions arise. Does AI-delivered empathy truly fulfill the emotional needs of human recipients? Can AI’s empathic capabilities help reduce the emotional labor of human providers? And how might it reshape the way people experience and value emotional connections in the digital age? This chapter explores these tensions by considering both the recipient and provider perspectives over the short and long term. In the short term, AI can alleviate emotional labor and offer effective emotional support, but it may also reduce humans’ empathic engagement. In the long term, reliance on AI may fundamentally alter social norms and emotional bonds, potentially leading to empathy atrophy or changing the nature of meaningful interactions. Overall, we aim to understand whether AI-driven empathy serves as a meaningful supplement to human interaction or a force that fundamentally alters the nature of emotional connection in the digital age.