Artificial intelligence, ethics, and diffused pivotality

  • 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.

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Author:Victor Klockmann, Alicia von Schenk, Marie-Claire Villeval
URN:urn:nbn:de:hebis:30:3-640162
URL:https://ssrn.com/abstract=4003065
DOI:https://doi.org/10.2139/ssrn.4003065
Parent Title (English):SAFE working paper ; No. 336
Series (Serial Number):SAFE working paper series (336)
Publisher:SAFE
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2021
Year of first Publication:2022
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/01/25
Tag:Artificial Intelligence; Big Data; Ethics; Experiment; Pivotality
Issue:December 21, 2021
Page Number:47
Note:
Financial research support from the Leibniz Institute for Financial Research SAFE, the Goethe University Frankfurt, and the LABEX CORTEX (ANR-11-LABX-0042) of Universite de Lyon, within the program Investissements Avenir (ANR-11-IDEX-007) operated by the French National Research Agency (ANR) is gratefully acknowledged.
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C4 Econometric and Statistical Methods: Special Topics / C49 Other
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht