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Training data debugging for the fairness of machine learning software

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References

2014Big Data's Disparate Impact3,046 cited
2015Certifying and Removing Disparate Impact1,827 cited
2018Mitigating Unwanted Biases with Adversarial Learning1,387 cited
2011Data preprocessing techniques for classification without discrimination1,304 cited
2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias884 cited
2010Three naive Bayes approaches for discrimination-free classification756 cited
2013Discrimination in online ad delivery751 cited
2017Fairness Beyond Disparate Treatment & Disparate Impact630 cited
2021Bias in machine learning software: why? how? what to do?224 cited
2018Automated directed fairness testing200 cited
2020Fairway: a way to build fair ML software140 cited
2018Software fairness138 cited
2020White-box fairness testing through adversarial sampling138 cited
2020Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness99 cited
2021"Ignorance and Prejudice" in Software Fairness80 cited

Cited by

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2023

Fairness-aware machine learning engineering: how far are we?

60 cited

2024Fairness Improvement with Multiple Protected Attributes: How Far Are We?46 cited

2023Towards Understanding Fairness and its Composition in Ensemble Machine Learning36 cited

2024Fairness for machine learning software in education: A systematic mapping study30 cited

2025Fairness-aware practices from developers’ perspective: A survey18 cited

2023Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML15 cited

2024NeuFair: Neural Network Fairness Repair with Dropout14 cited

2024A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligence14 cited

2023Causality-Aided Trade-Off Analysis for Machine Learning Fairness13 cited