Bias Mitigation Methods for Binary Classification Decision-Making
  Systems: Survey and Recommendations
Madeleine Waller Odinaldo Rodrigues Oana Cocarascu
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.