On Fairness, Diversity and Randomness in Algorithmic Decision Making

2017/06/30 by Nina Grgić-Hlača, Grgić-Hlača, Nina, Muhammad Bilal Zafar +5 · 1 citation
Neuroscience · Social Sciences · #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Psychology of Moral and Emotional Judgment

paper · pdf · doi:10.48550/arxiv.1706.10208

openalex publication_date 2017/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• OxEnsemble: Fair Ensembles for Low-Data Classification

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