Addressing Bias in AI: Ethical Concerns, Challenges, and Mitigation Strategies

Abstract

Abstract - Artificial Intelligence (AI) has revolutionized most industries, but it is also susceptible to bias, which gives rise to ethical problems and unintended consequences. AI bias can arise from biased training data, flawed algorithms, or institutional biases, which give rise to discriminatory judgments in healthcare, finance, law enforcement, and the workplace. This paper explores the reasons behind AI bias, its ethical dimensions, and how biased algorithms affect society. Besides, it also covers various mitigation strategies, including data preprocessing techniques, fair- aware algorithms, model auditing, and regulatory frameworks. Mitigation of AI bias is critical to constructing transparent, fair, and accountable AI systems that promote inclusivity and ethical decision-making. Key Words: AI bias, ethical concerns, fairness in AI, algorithmic bias, data preprocessing, transparency, model auditing, accountability, mitigation strategies