Title

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.

Department(s)

Psychological Science

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.

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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2022

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