Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL ACS Applied Energy Materials Pub Date : 2024-09-25 DOI:10.2196/62679
Jocelyn Shen, Daniella DiPaola, Safinah Ali, Maarten Sap, Hae Won Park, Cynthia Breazeal
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Abstract

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.

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对人工智能与人类体验的移情以及透明度在心理健康和社会支持聊天机器人设计中的作用:比较研究。
背景介绍同理心是我们与他人建立联系、获得心理健康和应对挑战的动力。随着生成式人工智能(AI)系统、心理健康聊天机器人和人工智能社交支持伴侣的兴起,了解共情如何在人类与人工智能叙述者的故事中展开,以及透明度如何在用户情感中发挥作用,显得尤为重要:我们旨在了解移情如何在人类编写的故事和人工智能编写的故事中发生转变,以及这些发现如何为将心理健康聊天机器人作为移情对象的伦理意义和以人为本的设计提供信息:我们对 985 名参与者进行了众包研究,他们每人都写了一个个人故事,然后对检索到的两个故事进行移情评级,其中一个故事是由语言模型编写的,另一个故事是由人类编写的。我们的研究对故事是由人类撰写还是由人工智能系统撰写进行了不同程度的披露,以了解透明的作者信息会如何影响对叙述者的移情。我们采用混合方法进行了分析:通过统计检验,我们比较了用户在不同条件下对故事的自述移情状态。此外,我们还对有关对故事的反应的开放式反馈进行了定性编码,以了解透明度如何以及为什么会影响对人类与人工智能讲故事者的共鸣:结果:我们发现,几乎在所有情况下,无论参与者是否知情,他们对人类编写的故事的共鸣都明显高于人工智能编写的故事(t196=7.07,P298=3.46,P494=-5.49,PC结论:我们的研究揭示了共鸣是如何影响人类与人工智能故事讲述者之间的关系的:我们的研究揭示了对人工智能或人类叙述者的移情是如何与文本呈现方式联系在一起的,从而为移情人工社会支持或心理健康聊天机器人的伦理考虑提供了信息。
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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