As information systems (IS) transition from deterministic rule-based engines to probabilistic machine learning architectures, the risk of systematic bias and disparate impact increases significantly. While advanced predictive models optimize across high-stakes domains—such as credit scoring, criminal justice, human resources, and biometric authentication, they frequently perpetuate historical inequities via proxy variables even when explicit sensitive attributes are omitted. This paper presents a detailed investigation into the theoretical foundations, mathematical formalisms, and systemic pipelines of "Fair AI." We propose an integrated sociotechnical framework spanning human perception, technological intervention, and organizational governance, fully incorporating recent advances in intelligent automation and secure architecture.

Muhammad Qasim, Laraib Tabish, Falak Ali,"Mitigating Algorithmic Bias in Information Systems: A Comprehensive Framework for Fair Artificial Intelligence" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(547-552) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.