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Bias mitigation methods for binary classification decision-making systemshave been widely researched due to the ever-growing importance of designingfair machine learning processes that are impartial and do not discriminateagainst individuals or groups based on protected personal characteristics. Inthis paper, we present a structured overview of the research landscape for biasmitigation methods, report on their benefits and limitations, and providerecommendations for the development of future bias mitigation methods forbinary classification.

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