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On Fairness, Diversity and Randomness in Algorithmic Decision Making

MPG-Autoren
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Grgić-Hlača,  Nina
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Zafar,  Muhammad Bilal
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Gummadi,  Krishna
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1706.10208.pdf
(Preprint), 517KB

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Zitation

Grgić-Hlača, N., Zafar, M. B., Gummadi, K., & Weller, A. (2017). On Fairness, Diversity and Randomness in Algorithmic Decision Making. Retrieved from http://arxiv.org/abs/1706.10208.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-DE3C-4
Zusammenfassung
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles.