# Appropriateness of Empathy in AI: A Signal-Cost Perspective

arXiv: https://arxiv.org/abs/2605.31340 · HTML: https://mlsanity.com/paper/2605.31340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 4 of 4)

- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [The Illusion of Empathy: How AI Chatbots Shape Conversation Perception](https://mlsanity.com/paper/2411.12877/) (2024)
- [Towards a Multidimensional Evaluation Framework for Empathetic Conversational Systems](https://mlsanity.com/paper/2407.18538/) (2024)

## Abstract (arXiv)

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.