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The study evaluates various bias mitigation strategies in AI healthcare systems, focusing on their impact on fairness and accuracy across two datasets: stroke prediction and breast cancer diagnosis. It proposes a framework for comparing single-stage and multi-stage interventions, revealing that no one-size-fits-all solution exists for bias reduction, as different strategies yield varying results depending on the context. Key findings indicate that Adversarial Debiasing and Reweighing are effective methods f

Author

akash singh negi

Language

EN