{"title":"Empathy by Design: The Influence of Trembling AI Voices on Prosocial Behavior","authors":"Fotis Efthymiou;Christian Hildebrand","doi":"10.1109/TAFFC.2023.3332742","DOIUrl":null,"url":null,"abstract":"Recent advances in artificial speech synthesis and machine learning equip AI-powered conversational agents, from voice assistants to social robots, with the ability to mimic human emotional expression during their interactions with users. One unexplored development is the ability to design machine-generated voices that induce varying levels of “shakiness” (i.e., trembling) in the agents’ voices. In the current work, we examine how the trembling voice of a conversational AI impacts users’ perceptions, affective experiences, and their subsequent behavior. Across three studies, we demonstrate that a trembling voice enhances the perceived psychological vulnerability of the agent, followed by a heightened sense of empathic concern, ultimately increasing people's willingness to donate in a prosocial charity context. We provide further evidence from a large-scale field experiment that conversational agents with a trembling voice lead to increased click-through rates and decreased costs-per-impression in an online charity advertising setting. These findings deepen our understanding of the nuanced impact of intentionally designed voices of conversational AI agents on humans and highlight the ethical and societal challenges that arise.","PeriodicalId":13131,"journal":{"name":"IEEE Transactions on Affective Computing","volume":"15 3","pages":"1253-1263"},"PeriodicalIF":9.6000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316625","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Affective Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10316625/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advances in artificial speech synthesis and machine learning equip AI-powered conversational agents, from voice assistants to social robots, with the ability to mimic human emotional expression during their interactions with users. One unexplored development is the ability to design machine-generated voices that induce varying levels of “shakiness” (i.e., trembling) in the agents’ voices. In the current work, we examine how the trembling voice of a conversational AI impacts users’ perceptions, affective experiences, and their subsequent behavior. Across three studies, we demonstrate that a trembling voice enhances the perceived psychological vulnerability of the agent, followed by a heightened sense of empathic concern, ultimately increasing people's willingness to donate in a prosocial charity context. We provide further evidence from a large-scale field experiment that conversational agents with a trembling voice lead to increased click-through rates and decreased costs-per-impression in an online charity advertising setting. These findings deepen our understanding of the nuanced impact of intentionally designed voices of conversational AI agents on humans and highlight the ethical and societal challenges that arise.
期刊介绍:
The IEEE Transactions on Affective Computing is an international and interdisciplinary journal. Its primary goal is to share research findings on the development of systems capable of recognizing, interpreting, and simulating human emotions and related affective phenomena. The journal publishes original research on the underlying principles and theories that explain how and why affective factors shape human-technology interactions. It also focuses on how techniques for sensing and simulating affect can enhance our understanding of human emotions and processes. Additionally, the journal explores the design, implementation, and evaluation of systems that prioritize the consideration of affect in their usability. We also welcome surveys of existing work that provide new perspectives on the historical and future directions of this field.