Algorithmic Bias in Artificial Intelligence: Strategies for Fairness and Ethical Decision-Making

Abstract

Algorithmic bias and fairness have emerged as critical concerns in the deployment of Artificial Intelligence (AI) systems across various high-stakes domains, including healthcare, finance, criminal justice, and recruitment. While AI is often considered objective, it can inadvertently perpetuate societal biases embedded in historical data or introduced during model development. This paper explores the origins, types, and impacts of algorithmic bias and reviews frameworks and methods designed to promote fairness in AI systems. Through a comprehensive literature review, we analyze key sources of bias across the AI lifecycle and examine approaches for detection and mitigation. The study highlights the ethical, social, and technical implications of biased AI, emphasizing the need for transparent, accountable, and ethically aligned practices. Findings suggest that balancing fairness, accuracy, and transparency is essential for fostering public trust and ensuring equitable outcomes. The paper concludes with recommendations for research, policy, and development practices that can advance responsible AI deployment.

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