As artificial intelligence chatbots offer increasingly sophisticated emotional support, society faces a profound question: can a machine truly empathize? Empathy and Artificial Intelligence provides the first comprehensive roadmap for this pivotal moment. Moving beyond simple binaries of 'hype' or 'doom,' this interdisciplinary volume unites leading psychologists, philosophers, and engineers to explore the tangled web of synthetic care. Key chapters investigate the 'AI Advantage' – where machines often outperform humans in perceived empathy – alongside the 'AI Penalty,' where discovering the artifice corrodes trust. The text navigates the distinct landscapes of text-based LLMs and embodied robots, addressing urgent ethical dilemmas and exploring whether reliance on AI risks the atrophy of our moral capacities or enables synthetic agents to scaffold stronger human relationships. Essential for researchers, students, and curious observers, this book investigates whether outsourcing our emotional labor saves us time, or costs us our humanity.
C. Daryl Cameron, Anat Perry, Shai Satran et al.· Cambridge University Press e...· 0 citations
Efforts to develop “empathic AI” often assume a universal definition for empathy—a singular, objective understanding of “human” experiences. This implies that achieving empathic AI is only a matter of optimization difficulty, where researchers should find the “correct” way to align AI systems with human-like empathy—essentially an engineering problem. In this chapter, we challenge this assumption. We begin by emphasizing the multifaceted nature of empathy. Then, we explore how variations in (1) the multiple forms of empathy and (2) the many groups we are embedded within present significant challenges in creating a universally applicable empathic AI. We discuss the difficulties of codifying empathy (an inherently context-dependent phenomenon) into AI systems, as well as the recent evidence for cultural biases and moral stereotypes in widely used Large Language Models (an important class of AI systems), highlighting the broader ethical, epistemic, and possibly existential issues inherent in designing machines that “understand” or “feel” others as humans do. We conclude by advocating for a more contextualized approach to empathic AI, one that is culture-aware, context-sensitive, and pluralistic, moving beyond the reductionist notion of “humans” as a monolith. We do not intend to address the question in our chapter’s title with a simple yes-or-no answer; instead, we advocate for asking questions such as “What kind of empathy?” and “Empathy for whom?” as researchers and engineers move toward developing more empathic generative language models.
Mohammad Atari, Firat Seker, Aliah Zewail· Cambridge University Press e...· 0 citations
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