Bad machines corrupt good morals
Nils Köbis,
Jean-François Bonnefon and
Iyad Rahwan
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Nils Köbis: Max Planck Institute for Human Development - Max-Planck-Gesellschaft
Iyad Rahwan: Max Planck Institute for Human Development - Max-Planck-Gesellschaft
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Abstract:
Machines powered by Artificial Intelligence (AI) are now influencing the behavior of humans in ways that are both like and unlike the ways humans influence each other. In light of recent research showing that other humans can exert a strong corrupting influence on people's ethical behavior, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioral science, human-computer interaction, and AI research. We propose that the main social roles through which both humans and machines can influence ethical behavior are (a) role model, (b) advisor, (c) partner, and (d) delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed (yet) the corrupting power of humans. However, AI agents acting as enablers of unethical behavior (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, indicating good reasons for worry. Based on these insights, we outline a research agenda that aims at providing more behavioral insights for better AI oversight.
Keywords: Machine behavior; Behavioral ethics; Corruption; Artificial intelligence (search for similar items in EconPapers)
Date: 2021-06
New Economics Papers: this item is included in nep-big, nep-cbe, nep-hme and nep-isf
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Citations: View citations in EconPapers (6)
Published in Nature Human Behaviour, 2021, 5 (6), pp.679-685. ⟨10.1038/s41562-021-01128-2⟩
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Related works:
Working Paper: Bad machines corrupt good morals (2023) 
Journal Article: Bad machines corrupt good morals (2021) 
Working Paper: Bad machines corrupt good morals (2021) 
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03292360
DOI: 10.1038/s41562-021-01128-2
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