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Cited by 738Psychology of Moral and Emotional JudgmentSocial Robot Interaction and HRIEthics and Social Impacts of AI

People are averse to machines making moral decisions.

Open access
Bigman, Yochanan E., Gray, Kurt·2018·Cognition
DOI 10.1016/j.cognition.2018.08.003
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Abstract

Do people want autonomous machines making moral decisions? Nine studies suggest that that the answer is 'no'-in part because machines lack a complete mind. Studies 1-6 find that people are averse to machines making morally-relevant driving, legal, medical, and military decisions, and that this aversion is mediated by the perception that machines can neither fully think nor feel. Studies 5-6 find that this aversion exists even when moral decisions have positive outcomes. Studies 7-9 briefly investigate three potential routes to increasing the acceptability of machine moral decision-making: limiting the machine to an advisory role (Study 7), increasing machines' perceived experience (Study 8), and increasing machines' perceived expertise (Study 9). Although some of these routes show promise, the aversion to machine moral decision-making is difficult to eliminate. This aversion may prove challenging for the integration of autonomous technology in moral domains including medicine, the law, the military, and self-driving vehicles.

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  • Algorithm aversion is especially acute in subjective, creative, or morally charged areas.

    “Later studies have demonstrated that the issue of algorithm aversion is especially acute in subjective, creative, or morally charged areas (Castelo et al., 2019;Bigman and Gray, 2018).”

    From The Impact of Generative AI on Personalized Content Marketing in E-Commerce (2025) · cites this paper as Bigman and Gray, 2018

  • Greater knowledge of algorithms is often linked to higher acceptance, but professionals may resist more strongly when they feel their expertise is being challenged.

    “Greater knowledge of algorithms is often linked to higher acceptance, but professionals may resist more strongly when they feel their expertise is being challenged (Bigman & Gray, 2018; Gaube et al., 2021).”

    From Algorithm aversion in academic libraries: User resistance to AI-enabled discovery and search services (2026) · cites this paper as Bigman & Gray, 2018

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Authors
Bigman, Y. E., & Gray, K.
Affiliations
University of North Carolina at Chapel Hill (US)
Corresponding author
Bigman, Yochanan E.
ORCID
Venue
Cognition
Publisher
Elsevier BV
Published
2018-12
Volume
181
Pages
21-34
ISSN
0010-0277 (ISSN-L)
DOI
10.1016/j.cognition.2018.08.003
OpenAlex
W2886363025
Cited by
738
References
12
Citations by year
2026: 158 · 2025: 145 · 2024: 123 · 2023: 153 · 2022: 68 · 2021: 51 · 2020: 29 · 2019: 10 · 2018: 1
FWCI
13.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Cognition (publishedVersion)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • UNC Libraries
Funders
  • National Science Foundation (1714298)
  • Charles Koch Foundation
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
ethical decision-making, autonomous vehicles, autonomous weapons, clinical decision-making, legal decision-making, advisory role, perceived expertise, perceived experience, human-automation interaction
MeSH terms
Adult, Affect, Artificial Intelligence, Decision Making, Female, Humans, Male, Morals, Young Adult

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