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Artificial Intelligence (AI), bias, fairness in machine learning, machine learning ethics, algorithmic bias, data bias, mitigation strategies, explainable AI (XAI), fair representation, data preprocessing, fairness metrics, algorithmic fairness, Transparency, accountability, responsible AI development yaman barakat Artificial Intelligence (AI), bias, fairness in machine learning, machine learning ethics, algorithmic bias, data bias, mitigation strategies, explainable AI (XAI), fair representation, data preprocessing, fairness metrics, algorithmic fairness, Transparency, accountability, responsible AI development yaman barakat

Addressing Bias in AI

Artificial intelligence is reshaping society, but its rapid growth brings increasing concerns about algorithmic bias and fairness. This study investigates the root causes of bias in AI systems and examines approaches for developing more equitable machine learning models. By addressing these challenges, the goal is to build AI that supports inclusion, avoids discrimination, and contributes to a more just and responsible technological future.

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