Ensuring Fairness: Analyzing Algorithmic Bias in AI-Powered Decision-Making Systems

Abstract

Abstract: As artificial intelligence (AI) becomes increasingly integral to decision-making processes in domains such as hiring, lending, policing, and healthcare, concerns surrounding algorithmic bias and fairness are rising. AI systems often reflect and amplify historical and societal biases present in the data they are trained on or embedded in their design. This paper critically explores the sources and manifestations of algorithmic bias and assesses their impact on various sectors. Mitigation strategies are discussed through the lens of data preprocessing, model auditing, and post-processing correction methods. Case studies are analyzed to illustrate the real-world implications and potential of bias-aware AI development. The paper concludes with a call for multidisciplinary collaboration, transparent evaluation protocols, and policy frameworks to ensure equitable AI outcomes.

Keywords: Algorithmic bias, AI fairness, discrimination, responsible AI, bias mitigation, ethical AI, machine learning, model auditing