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maxhort authored Sep 26, 2023
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<h1 class = "join-h1"> Software Fairness</h1>
<h2 class="funding-h2">Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey</h2>

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<h3 class= "funding-box-h3"> This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine
Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers.
These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing,
post-processing) and the technique they apply. We investigate how existing bias mitigation methods are
evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the
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