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arXiv · 2305.20020

Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations

Madeleine Waller·Odinaldo Rodrigues·Oana Cocarascu
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Abstract

Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the research landscape for bias mitigation methods, report on their benefits and limitations, and provide recommendations for the development of future bias mitigation methods for binary classification.

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Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu. 2023-05-31. Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations. https://arxiv.org/abs/2305.20020

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