Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study
Background: Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds towa...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications Inc.
2025
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| Online Access: | https://hdl.handle.net/1721.1/158124 |
| _version_ | 1824458193671028736 |
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| author | Shen, Jocelyn DiPaola, Daniella Ali, Safinah Sap, Maarten Park, Hae Won Breazeal, Cynthia |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Shen, Jocelyn DiPaola, Daniella Ali, Safinah Sap, Maarten Park, Hae Won Breazeal, Cynthia |
| author_sort | Shen, Jocelyn |
| collection | MIT |
| description | Background:
Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.
Objective:
We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.
Methods:
We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user’s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.
Results:
We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=–5.49, P<.001, Cohen d=0.36).
Conclusions:
Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots. |
| first_indexed | 2025-02-19T04:22:00Z |
| format | Article |
| id | mit-1721.1/158124 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:22:00Z |
| publishDate | 2025 |
| publisher | JMIR Publications Inc. |
| record_format | dspace |
| spelling | mit-1721.1/1581242025-01-28T21:45:30Z Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study Shen, Jocelyn DiPaola, Daniella Ali, Safinah Sap, Maarten Park, Hae Won Breazeal, Cynthia Massachusetts Institute of Technology. Media Laboratory Background: Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. Objective: We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. Methods: We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user’s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. Results: We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=–5.49, P<.001, Cohen d=0.36). Conclusions: Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots. 2025-01-28T21:45:28Z 2025-01-28T21:45:28Z 2024 2025-01-28T21:38:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/158124 Shen, Jocelyn, DiPaola, Daniella, Ali, Safinah, Sap, Maarten, Park, Hae Won et al. 2024. "Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study." JMIR Mental Health, 11. en https://doi.org/10.2196/62679 JMIR Mental Health Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf JMIR Publications Inc. JMIR Publications Inc. |
| spellingShingle | Shen, Jocelyn DiPaola, Daniella Ali, Safinah Sap, Maarten Park, Hae Won Breazeal, Cynthia Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title | Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title_full | Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title_fullStr | Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title_full_unstemmed | Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title_short | Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study |
| title_sort | empathy toward artificial intelligence versus human experiences and the role of transparency in mental health and social support chatbot design comparative study |
| url | https://hdl.handle.net/1721.1/158124 |
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