How Do People Judge the Immorality of Artificial Intelligence Versus Humans Committing Moral Wrongs in Real-World Situations?
Abstract
In general, people will judge a morally wrong behavior when perpetrated by an artificial intelligence (AI) as still being wrong. But moral judgements are complex, and therefore we explore how moral judgements made about AIs differ from those made about humans in real-world situations. In contrast to much of the current research on the morality of AIs, we examine real-world encounters where an AI commits a moral wrong as reported by participants in previous research. We adapt these to create nearly identical scenarios with human perpetrators. In Study 1, across scenarios, humans are perceived as more wrong, intentional, and blameworthy compared to AIs. In Study 2, we replicate those results and find that showing the participants the contrasting scenario - showing the AI scenario when one is rating the human scenario or vice versa - does not have a significant effect on moral judgements. An exploratory word-frequency analysis and illustrative quotes from participants' open-ended explanations show that AIs are more often denied agency and perceived as programmed and therefore unintentional in producing the moral outcome. In contrast, humans are perceived as highly agentic and intentional, either fully responsible for the wrongdoing or not morally culpable because the behavior was perceived as a mistake.
Recommended Citation
Wilson, A., Stefanik, C., & Shank, D. B. (2022). How Do People Judge the Immorality of Artificial Intelligence Versus Humans Committing Moral Wrongs in Real-World Situations?. Computers in Human Behavior Reports, 8 Elsevier.
The definitive version is available at https://doi.org/10.1016/j.chbr.2022.100229
Department(s)
Psychological Science
Keywords and Phrases
Algorithms; Artificial Intelligence; Intentional; Machines; Moral Judgment
International Standard Serial Number (ISSN)
2451-9588
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2022
Comments
This study was supported by the First Year Research Experience program at Missouri University of Science and Technology as well as the Army Research Office under Grant Number W911NF-19-1-0246.