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Artificial intelligence, distributional fairness, and pivotality

Author

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  • Victor Klockmann

    (JMU - Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany], Goethe University Frankfurt = Goethe-Universität Frankfurt am Main, Max Planck Institute for Human Development - Max-Planck-Gesellschaft)

  • Alicia von Schenk

    (JMU - Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany], Goethe University Frankfurt = Goethe-Universität Frankfurt am Main, Max Planck Institute for Human Development - Max-Planck-Gesellschaft)

  • Marie Claire Villeval

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique)

Abstract

In the field of machine learning, the decisions of algorithms depend on extensive training data contributed by numerous, often human, sources. How does this property affect the social nature of human decisions that serve to train these algorithms? By experimentally manipulating the pivotality of individual decisions for a supervised machine learning algorithm, we show that the diffusion of responsibility weakened revealed social preferences, leading to algorithmic models favoring selfish decisions. Importantly, this phenomenon cannot be attributed to shifts in incentive structures or the presence of externalities. Rather, our results suggest that the expansive nature of Big Data fosters a sense of diminished responsibility and serves as an excuse for selfish behavior that impacts individuals and the whole society.

Suggested Citation

  • Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2025. "Artificial intelligence, distributional fairness, and pivotality," Post-Print hal-05165240, HAL.
    • Handle: RePEc:hal:journl:hal-05165240
    DOI: 10.1016/j.euroecorev.2025.105098
    Note: View the original document on HAL open archive server: https://hal.science/hal-05165240v1
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