Mitigating Algorithmic Bias and Ensuring Fairness in Machine Learning
收藏NIAID Data Ecosystem2026-05-02 收录
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https://zenodo.org/record/14999227
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资源简介：
This research investigates algorithmic bias in machine learning models and proposes a bias mitigation framework using re-weighting techniques. The study applies fairness evaluation metrics like the Disparate Impact Ratio (DIR) on the UCI Adult dataset and demonstrates improvements in fairness while maintaining model accuracy.
创建时间：
2025-03-10
