Mitigating Bias in Artificial Intelligence: Methods and Challenges

Abstract

The extensive application of Artificial Intelligence (AI) across the core domains of society has brought forth massive challenges towards prejudice, embedding discrimination, feeding inequalities, and eroding trust among citizens. This report explores the multi-dimensioned aspect of AI systems' prejudice by understanding the causes of the phenomenon in terms of data, algorithms, and end-user interface and also exploring its social implications and normative concerns. We give a comprehensive overview of existing state-of-the-art bias detection methods, i.e., statistical approaches, explainability tools, and fairness measures, and discuss mitigation techniques in pre-processing, in-processing, and post-processing. Challenges persist, such as negative fairness-accuracy trade-offs, limited standardized benchmarks, and need for inter-disciplinary efforts. Through case studies and regulatory analysis, we determine best practices and novel frameworks that will propel fair AI. The paper concludes by offering the directions of future research, emphasizing the necessity of open, transparent, accountable, and inclusive approaches to prevent AI systems from deviating from moral principles and societal values.