Detecting and Mitigating Bias in AI Systems: A Methodological Approach to Social Inequality

Artificial intelligence (AI) systems are increasingly embedded in decisions that affect health, employment, housing, justice, and social policy. Rather than neutral tools, they reflect the data on which they are trained, the objectives they optimize, and the contexts in which they operate, reinforcing and amplifying existing social inequalities. This paper addresses two interrelated questions: how can we detect bias across the AI lifecycle, and what technical and governance strategies can effectively mitigate it? Drawing on critical social science perspectives, empirical case studies from healthcare, housing, policing, and labor markets, and recent advances in fair machine learning (including causal debiasing, synthetic data generation, and fair representation learning), the paper develops a conceptual framework, grounded in sociological theories, spanning data collection, preprocessing, model training, deployment, and post-deployment monitoring. As an original contribution, the paper proposes two complementary analytical frameworks: a Social Harm-Intervention Mapping that recasts mitigation strategies in terms of the types of inequality they address, and an Accountability Matrix that maps agency and accountability gaps across social actors and pipeline stages. The paper argues that these frameworks provide social researchers and policymakers with more actionable guidance than conventional pipeline taxonomies because they foreground the social and institutional dimensions of AI-induced inequality rather than treating bias as a purely technical problem.