Ethical AI Framework for Bias Mitigation in Machine Learning Algorithms
Abstract
As machine learning systems are increasingly deployed in critical applications, addressing algorithmic bias is essential to ensure fairness and equity. This paper presents an ethical AI framework for identifying and mitigating biases in machine learning algorithms. The framework includes bias detection tools, fairness-aware optimization techniques, and guidelines for ethical model deployment. Case studies in hiring, credit scoring, and healthcare are used to evaluate the framework’s effectiveness, showing significant reductions in biased outcomes without sacrificing model performance. This research underscores the importance of integrating ethical considerations into AI development to build systems that are transparent, inclusive, and socially responsible.
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