Fairness at Risk: Where Bias Emerges in Machine Learning

ABSTRACT Artificial intelligence and machine learning (ML) now shape decisions in healthcare, finance and security, but they can reproduce historical prejudice and inequality. Bias in training data and in model implementation can amplify harm, especially for racial and gender minorities. Despite sustained research on fairness, mitigation in real‐world systems remains uneven, in part because stakeholders lack a shared and precise grasp of core notions, including bias, prejudice, discrimination and fairness. As a result, technical interventions are sometimes adopted without consistent conceptual grounding and reporting. This article addresses that problem by providing a knowledge base that aligns key concepts with empirical evidence and lifecycle stages. We conduct a scoping review to map sources of bias across the ML lifecycle and to identify forms of prejudice and discrimination associated with the use of sensitive attributes. We synthesize qualitative and quantitative evidence and introduce a conceptual model for organizing these findings. Our contributions are threefold: a refined lifecycle taxonomy of bias sources that introduces two additional types and spans all development stages; the explicit treatment of cognitive bias as a cross‐cutting meta‐bias; and an analysis of prejudice and discrimination that compiles a legally grounded catalogue of sensitive attributes and discusses their concepts and issues. Together, these results provide an integrated view of where and how bias emerges, and they support future research, evaluation and governance work on fairness in ML.