Jul 7, 2022 · We present a large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers.
We present a large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers.
Jun 18, 2024 · Bias mitigation methods include pre-processing, in-processing, and post-processing [2] . Among them, Reweighting (RW) is a widely used pre- ...
A large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers, evaluated with 11 ML ...
May 27, 2023 · Software bias is an increasingly important operational concern for software engineers. We present a largescale, comprehensive empirical ...
Article · A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers · An open access version is available from UCL Discovery.
The empirical coverage is much more comprehensive, covering the largest numbers of bias mitigation methods, evaluation metrics, and fairness-performance ...
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine. Learning (ML) models. We collect a total of 341 ...
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Abstract—This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models.