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On Fairness, Diversity and Randomness in Algorithmic Decision Making

Grgić-Hlača, N and Zafar, MB and Gummadi, KP and Weller, A On Fairness, Diversity and Randomness in Algorithmic Decision Making. (Unpublished)

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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.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML cs.LG
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 15 Nov 2017 20:14
Last Modified: 18 Feb 2021 18:14