Comprehensively Assessing the Landscape of Algorithmic Bias and Fairness Considerations in Modern AI Systems

Abstract

Algorithmic bias and fairness have become pressing concerns as artificial intelligence (AI) systems are increasingly deployed in high-stakes domains like healthcare, criminal justice, and employment. Left unchecked, biases in data and algorithms can lead to discriminatory and unethical outcomes. This paper provides a comprehensive review of the current landscape of algorithmic bias and fairness research across computer science, statistics, and related disciplines. We summarize key sources of bias, survey mathematical definitions of fairness, examine state-of-the-art techniques for bias mitigation, and highlight outstanding challenges and open problems. Our analysis reveals a complex, multi-faceted problem requiring interdisciplinary perspectives. We find that while substantial progress has been made, especially on technical bias mitigation techniques, significant gaps remain in translating methods into practice and understanding sources of bias that stem from broader societal inequities. We conclude with recommendations for advancing algorithmic fairness research and deploying fairer AI systems, emphasizing holistic solutions that account for legal, ethical, and social contexts.