Ethical Challenges in AI: A Framework for Fair and Bias-Free Machine Learning Models
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Abstract
As AI systems become increasingly integrated into decision-making processes, concerns about algorithmic bias and ethical implications have gained prominence. This paper proposes a fairness-driven framework that incorporates bias detection and mitigation techniques to ensure ethical AI development. The study evaluates various fairness-enhancing methods, including adversarial debiasing and re-weighting strategies, across multiple datasets. The findings highlight the effectiveness of these techniques in reducing bias while maintaining model performance, contributing to the development of more ethical and responsible AI systems.
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References
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