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Reciprocity in Machine Learning

Author

Listed:
  • Mukund Sundararajan

    (Google)

  • Walid Krichene

    (Google Research)

Abstract

Machine learning is pervasive. It powers recommender systems such as Spotify, Instagram and YouTube, and health-care systems via models that predict sleep patterns, or the risk of disease. Individuals contribute data to these models and benefit from them. Are these contributions (outflows of influence) and benefits (inflows of influence) reciprocal? We propose measures of outflows, inflows and reciprocity building on previously proposed measures of training data influence. Our initial theoretical and empirical results indicate that under certain distributional assumptions, some classes of models are approximately reciprocal. We conclude with several open directions.

Suggested Citation

  • Mukund Sundararajan & Walid Krichene, 2022. "Reciprocity in Machine Learning," Papers 2202.09480, arXiv.org.
    • Handle: RePEc:arx:papers:2202.09480
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    References listed on IDEAS

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    1. Ernst Fehr & Simon Gächter, 2000. "Fairness and Retaliation: The Economics of Reciprocity," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 159-181, Summer.
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