Bias Mitigation in AI Models: A Framework for Fairness-Aware Data Preprocessing
Abstract
Bias in AI models can lead to unfair outcomes, particularly in sensitive domains like hiring, lending, and healthcare. This paper presents a fairness-aware data preprocessing framework to mitigate bias at the data level before model training. The framework employs techniques such as reweighting, resampling, and adversarial debiasing to ensure equitable representation across demographic groups. Case studies in credit scoring and job applicant filtering highlight the framework’s effectiveness in reducing bias while maintaining model performance. These findings emphasize the importance of ethical considerations in AI development.
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