Abstract
Artificial agents such as robots, chatbots, and artificial intelligence systems can be the perpetrators of a range of moral violations traditionally limited to human actors. This paper explores how people perceive the same moral violations differently for artificial agent and human perpetrators by addressing three research questions: How wrong are moral foundation violations by artificial agents compared to human perpetrators? Which moral foundations do artificial agents violate compared to human perpetrators? What leads to increased blame for moral foundation violations by artificial agents compared to human perpetrators? We adapt 18 human-perpetrated moral violation scenarios that differ by the moral foundation violated (harm, unfairness, betrayal, subversion, degradation, and oppression) to create 18 agent-perpetrated moral violation scenarios. Two studies compare human-perpetrated to agent-perpetrated scenarios. They reveal that agent-perpetrated violations are more often perceived as not wrong or violating a different foundation than their human counterparts. People are less likely to classify violations by artificial agents as oppression and subversion, the foundations that deal the most with group hierarchy. Finally, artificial agents are blamed less than humans across moral foundations, and this blame is based more on the agent's ability and intention for every moral foundation except harm.
Recommended Citation
Maninger, T., & Shank, D. B. (2022). Perceptions of Violations by Artificial and Human Actors across Moral Foundations. Computers in Human Behavior Reports, 5 Elsevier.
The definitive version is available at https://doi.org/10.1016/j.chbr.2021.100154
Department(s)
Psychological Science
Keywords and Phrases
Artificial agents; Artificial intelligence; Moral foundations; Moral wrongs; Morality; Theory of blame
International Standard Serial Number (ISSN)
2451-9588
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Mar 2022
Comments
Research was sponsored by the Army Research Office and was accomplished under Grant Number W911NF-19-1-0246 to Dr. Daniel B. Shank.