Year: 2026
Type: book-chapter
Abstract: 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.
Authors: Mohammad Atari, Fırat Şeker, Aliah Zewail
Institution: University of Massachusetts Amherst
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4414552496
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