Empathy in Explanation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912507067629568 |
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| author | Collins, Katherine M. Chandra, Kartik Weller, Adrian Ragan-Kelley, Jonathan Tenenbaum, Joshua B. |
| author_facet | Collins, Katherine M. Chandra, Kartik Weller, Adrian Ragan-Kelley, Jonathan Tenenbaum, Joshua B. |
| contents | Why do we give the explanations we do? Recent work has suggested that we should think of explanation as a kind of cooperative social interaction, between a why-question-asker and an explainer. Here, we apply this perspective to consider the role that emotion plays in this social interaction. We develop a computational framework for modeling explainers who consider the emotional impact an explanation might have on a listener. We test our framework by using it to model human intuitions about how a doctor might explain to a patient why they have a disease, taking into account the patient's propensity for regret. Our model predicts human intuitions well, better than emotion-agnostic ablations, suggesting that people do indeed reason about emotion when giving explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empathy in Explanation Collins, Katherine M. Chandra, Kartik Weller, Adrian Ragan-Kelley, Jonathan Tenenbaum, Joshua B. Human-Computer Interaction Artificial Intelligence Why do we give the explanations we do? Recent work has suggested that we should think of explanation as a kind of cooperative social interaction, between a why-question-asker and an explainer. Here, we apply this perspective to consider the role that emotion plays in this social interaction. We develop a computational framework for modeling explainers who consider the emotional impact an explanation might have on a listener. We test our framework by using it to model human intuitions about how a doctor might explain to a patient why they have a disease, taking into account the patient's propensity for regret. Our model predicts human intuitions well, better than emotion-agnostic ablations, suggesting that people do indeed reason about emotion when giving explanations. |
| title | Empathy in Explanation |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21081 |