Chaining fairness mitigation algorithms across ML lifecycle stages sometimes reduces bias more than single-stage application, but the evidence here is under-specified and partly circular.
The effects of data preprocessing on probability of default model fairness
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In the context of financial credit risk evaluation, the fairness of machine learning models has become a critical concern, especially given the potential for biased predictions that disproportionately affect certain demographic groups. This study investigates the impact of data preprocessing, with a specific focus on Truncated Singular Value Decomposition (SVD), on the fairness and performance of probability of default models. Using a comprehensive dataset sourced from Kaggle, various preprocessing techniques, including SVD, were applied to assess their effect on model accuracy, discriminatory power, and fairness.
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Analyzing Fairness of Computer Vision and Natural Language Processing Models
Chaining fairness mitigation algorithms across ML lifecycle stages sometimes reduces bias more than single-stage application, but the evidence here is under-specified and partly circular.