Artikel

People prefer moral discretion to algorithms: Algorithm aversion beyond intransparency

We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to algorithms that rigidly apply exogenously given human-created fairness principles to specific cases. In the second study, we found that people do not prefer humans to algorithms because they appreciate flesh-and-blood decision-makers per se, but because they appreciate humans' freedom to transcend fairness principles at will. Our results contribute to a deeper understanding of algorithm aversion. They indicate that emphasizing the transparency of algorithms that clearly follow fairness principles might not be the only element for fostering societal algorithm acceptance and suggest reconsidering certain features of the decision-making process.

Sprache
Englisch

Erschienen in
Journal: Philosophy & Technology ; ISSN: 2210-5441 ; Volume: 35 ; Year: 2022 ; Issue: 1 ; Dordrecht: Springer

Klassifikation
Landwirtschaft, Veterinärmedizin
Technological Change: Choices and Consequences; Diffusion Processes
Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡
Design of Experiments: Laboratory, Individual
Thema
algorithm aversion
artificial intelligence
moral discretion
behavioral ethics

Ereignis
Geistige Schöpfung
(wer)
Jauernig, Johanna
Uhl, Matthias
Walkowitz, Gari
Ereignis
Veröffentlichung
(wer)
Springer
(wo)
Dordrecht
(wann)
2022

DOI
doi:10.1007/s13347-021-00495-y
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Jauernig, Johanna
  • Uhl, Matthias
  • Walkowitz, Gari
  • Springer

Entstanden

  • 2022

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