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Distributing Blame Among Multiple Entities When Autonomous Technologies Cause Harm

Bibliographic Data

ID21453942
AuthorsRyan M McManus (0000-0002-1793-1595, Boston College, Chestnut Hill, MA, USA, corresponding author), Catherine C Mesick (California State University, Northridge), Abraham M Rutchick (0000-0003-3402-0934, California State University, Northridge)
Year2025
Volume51
Issue10
Pages2068-2084
Publication date2025-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePersonality and Social Psychology Bulletin (JOURNAL)
Journal identifiersISSN: 0146-1672 • E-ISSN: 1552-7433
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/01461672241238303
PMID38613365
OpenAlexW4394786028
LanguageEN
References cited34

As autonomous technology emerges, new variations in old questions arise. When autonomous technologies cause harm, who is to blame? The current studies compare reactions toward harms caused by human-controlled vehicles (HCVs) or human soldiers (HSs) to identical harms by autonomous vehicles (AVs) or autonomous robot soldiers. Drivers of HCVs, or HSs, were blamed more than mere users of AVs or HSs who outsourced their duties to ARSs. However, as human drivers/soldiers became less involved in (or were unaware of the preprogramming that led to) the harm, blame was redirected toward other entities (i.e., manufacturers and the tech company’s executives), showing the opposite pattern as human drivers/soldiers. Results were robust to how blame was measured (i.e., degrees of blame versus apportionment of total blame). Overall, this research furthers the blame literature, raising questions about why, how (much), and to whom blame is assigned when multiple agents are potentially culpable

Apportionment · Blame · Business · Computer security · Harm · Political science · Computer Science · Ethics and Social Impacts of AI · Law · Psychology · Psychology of Moral and Emotional Judgment · Risk Perception and Management · Social Psychology

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Citation velocityhistorical
Highly citedNo

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