Mitigating Algorithmic Bias and Ensuring Fairness in Machine Learning
收藏Zenodo2025-03-10 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.14999228
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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.
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Zenodo创建时间：
2025-03-10
