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Addressing Bias in AI
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

This Article explores the sources of bias in AI systems and delves into various methods and practices for developing fairer machine learning models. By understanding these methods, we can strive to create AI systems that are inclusive, unbiased, and contribute to a more equitable future, Artificial intelligence (AI) systems are rapidly transforming various aspects of our lives. However, concerns regarding bias in AI algorithms are growing. These biases can lead to discriminatory outcomes, hindering the ethical and responsible implementation of AI.

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