Algorithmic Accountability in High-Stakes Decision Systems: A Review of Fairness Metrics, Bias Mitigation Techniques, and Transparency Requirements across Healthcare, Criminal Justice, and Financial Services

Abstract

Artificial intelligence systems deployed in high-stakes decision contexts, including healthcare diagnosis, criminal justice risk assessment, credit underwriting, and employment screening, exert profound influence over individual life outcomes. The progressive deployment of these systems since the mid-2010s has revealed systematic patterns of discriminatory behavior rooted in biased training data, biased algorithm design, and inadequate transparency mechanisms that prevent affected individuals from understanding, contesting, or seeking redress for algorithmically mediated decisions that affect them adversely. This review surveys the body of knowledge accumulated on algorithmic fairness, bias mitigation, and AI transparency from the foundational contributions of the 2016 to 2022 period through recent practical implementation work. We systematically review the major fairness metric families, including group fairness, error-rate parity, calibration, and individual fairness, characterize the impossibility results that constrain their simultaneous satisfaction, and survey preprocessing, in-processing, and post-processing bias mitigation techniques documented in the peer-reviewed literature. We analyze transparency and explainability mechanisms including model cards, datasheets for datasets, and post-hoc explanation methods, assessing their adequacy against the accountability requirements of GDPR Article 22, the EU AI Act high-risk system provisions, and the NIST AI Risk Management Framework. Sector-specific analyses of healthcare, criminal justice, and financial services reveal that domain characteristics, including the nature of the protected attributes at risk, the reversibility of adverse decisions, and the regulatory instruments applicable, require tailored fairness and accountability frameworks rather than generic deployments of domain-agnostic metrics. Recent applied implementation work demonstrates that these frameworks are operationally deployable rather than theoretically aspirational.