Appropriateness of Empathy in AI: A Signal-Cost Perspective

2026-05-29

AI Summary

Current AI systems often exhibit empathy that is either excessive or insufficient, leading users to perceive them as manipulative or indifferent. This work introduces the economic concept of signaling cost into AI empathy research for the first time, proposing a user-need-centered paradigm for evaluating empathic appropriateness. Grounded in signaling theory and integrating natural language processing with discourse analysis, the study develops a quantifiable set of proxy indicators—termed Signal Cost Proxies—that systematically map affective, cognitive, and relational empathy across three dimensions: emotional richness, perspective-taking, and contextual alignment. Moving beyond traditional binary assessments of empathy presence or absence, this framework offers a novel pathway toward more natural and credible human–AI empathic interaction.

Abstract

The appropriateness of empathy in AI has emerged as a critical concern, as excessive empathy risks seeming manipulative while insufficient empathy appears dismissive. While prior research has explored how to quantify empathy in AI, few studies examine whether such empathy is contextually appropriate. This paper introduces an economic perspective by applying signaling theory to human-AI conversations. We propose Signal Cost Proxies (emotional richness, perspective-taking, and contextual tailoring) mapped to affective, cognitive, and associative empathy. This multidimensional framework enables systematic evaluation of empathy not just by presence, but by its appropriateness relative to user demand.

Problem

• empathy appropriateness
• human-AI interaction
• signaling theory
• contextual relevance
• AI empathy

Innovation

• signal cost
• empathy appropriateness
• human-AI interaction
• signaling theory
• contextual tailoring