Artificial intelligence, ethics, and diffused pivotality

Victor Klockmann, Alicia von Schenk and Marie Claire Villeval

No 336, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

Abstract: With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual's decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one's and others' fate.

Keywords: Artificial Intelligence; Big Data; Pivotality; Ethics; Experiment (search for similar items in EconPapers)
JEL-codes: C49 C91 D10 D63 D64 O33 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
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https://www.econstor.eu/bitstream/10419/249179/1/178624666X.pdf (application/pdf)

Related works:
Working Paper: Artificial Intelligence, Ethics, and Diffused Pivotality (2021)
Working Paper: Artificial Intelligence, Ethics, and Diffused Pivotality (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:336

DOI: 10.2139/ssrn.4003065

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