Bias Mitigation in Machine Learning: Techniques and Challenges
Abstract
Bias in machine learning models can lead to unfair outcomes in critical applications such as hiring, lending, and criminal justice. This paper surveys methods for bias detection and mitigation, including re-sampling, re-weighting, and adversarial debiasing. We provide case studies from real-world datasets and propose a framework for evaluating fairness in ML models.
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