Psychological and Brain Responses to Artificial Intelligence's Violation of Community Ethics.

Journal: Cyberpsychology, behavior and social networking
Published Date: May 17, 2024

Abstract

Human moral reactions to artificial intelligence (AI) agents' behavior constitute an important aspect of modern-day human-AI relationships. Although previous studies have mainly focused on autonomy ethics, this study investigates how individuals judge AI agents' violations of community ethics (including betrayals and subversions) compared with human violations. Participants' behavioral responses, event-related potentials (ERPs), and individual differences were assessed. Behavioral findings reveal that participants rated AI agents' community-violating actions less morally negative than human transgressions, possibly because AI agents are commonly perceived as having less agency than human adults. The ERP N1 component showed the same pattern with moral rating scores, indicating the modulation effect of human-AI differences on initial moral intuitions. Moreover, the level of social withdrawal correlated with a smaller N1 in the human condition but not in the AI condition. The N2 and P2 components were sensitive to the difference between the loyalty/betrayal and authority/subversion domains but not human/AI differences. Individual levels of moral sense and autistic traits also influenced behavioral data, especially on the loyalty/betrayal domain. In our opinion, these findings offer insights for predicting moral responses to AI agents and guiding ethical AI development aligned with human moral values.

Authors

  • Yue He
    Department of Breast Surgery, Hunan Cancer Hospital, Changsha, Hunan, China.
  • Ruolei Gu
    Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences Beijing, China.
  • Guangzhi Deng
    Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, People's Republic of China.
  • Yongling Lin
    State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China.
  • Tian Gan
    Guilin University of Electronic Technology, School of Computer Science and Information Security, Guilin, 541004, China.
  • Fang Cui
    School of Psychology, Shenzhen University, Shenzhen, People's Republic of China.
  • Chao Liu
    Anti-Drug Technology Center of Guangdong Province, National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou 510230, China.
  • Yue-Jia Luo
    Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China; Medical School, Kunming University of Science and Technology, Kunming, China; Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China. Electronic address: luoyj@szu.edu.cn.

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