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: Survey and Recommendations

Machine Learning 2023-06-01 v1 Artificial Intelligence Computers and Society

Authors: Madeleine Waller , Odinaldo Rodrigues , Oana Cocarascu

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.

Keywords

fairness in machine learning classification algorithm selection

Cite

@article{arxiv.2305.20020,
  title  = {Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations},
  author = {Madeleine Waller and Odinaldo Rodrigues and Oana Cocarascu},
  journal= {arXiv preprint arXiv:2305.20020},
  year   = {2023}
}

Comments

22 pages

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