Algorithmic Strategies for Mitigating Bias in AI-driven Decision-Making Systems

Abstract

The pervasive integration of artificial intelligence (AI) in decision-making processes has exacerbated concerns regarding algorithmic bias, leading to significant ethical and operational challenges. This study investigates various algorithmic strategies aimed at identifying and mitigating biases inherent in AI systems. Employing a quantitative approach, we analyzed multiple datasets across diverse application domains to evaluate the effectiveness of these strategies. Our findings reveal that implementing specific debiasing techniques not only enhances the fairness of AI outcomes but also improves overall system performance. This research provides a framework for practitioners aiming to develop more equitable AI-driven solutions across sectors, highlighting its critical importance in fostering trust and accountability in intelligent systems.