Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations
This is an incremental work that synthesizes existing research to guide practitioners in designing fair machine learning systems.
The paper provides a structured survey of bias mitigation methods for binary classification decision-making systems, summarizing their benefits and limitations and offering recommendations for future development.
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.