Fairness, Accountability, and Transparency in AI-Based Decision Systems: A Systematic Review and Conceptual Framework

Sabnam Pradhan; Sandhya M; Dayana Sherine P M; Ghousiya Y

Fairness, Accountability, and Transparency in AI-Based Decision Systems: A Systematic Review and Conceptual Framework

Author(s)	Prof. Sabnam Pradhan, Prof. Sandhya M, Prof. Dayana Sherine P M, Prof. Ghousiya Y
Country	India
Abstract	The rapid deployment of Artificial Intelligence (AI) in high-stakes decision-making domains has intensified concerns regarding fairness, accountability, and transparency (FAT) in algorithmic systems. AI-driven models increasingly influence critical decisions in healthcare, finance, criminal justice, recruitment, and public administration, directly affecting individuals and communities. While these systems offer enhanced efficiency, scalability, and predictive accuracy, they also pose significant risks, including algorithmic bias, lack of explainability, and unclear responsibility for automated decisions. These challenges raise ethical, legal, and societal concerns, particularly when AI systems operate in sensitive socio-technical contexts. This paper presents a systematic review of interdisciplinary research addressing FAT principles in AI-based decision systems. It synthesizes key contributions from computer science, ethics, law, and social sciences to identify prevailing fairness definitions, accountability frameworks, and transparency mechanisms. The review highlights critical gaps, including fragmented approaches to FAT implementation, limited integration across the AI lifecycle, and persistent trade-offs between transparency, performance, and proprietary constraints. To address these limitations, this study proposes a lifecycle-based conceptual framework that embeds fairness, accountability, and transparency across all stages of AI development, from problem formulation and data collection to deployment and governance. This framework aims to support the design and deployment of equitable, trustworthy, and socially responsible AI systems.
Keywords	Fairness, Accountability, Transparency, AI-based Decision Systems, Algorithmic Bias, Explainable AI, Ethical AI, Socio-technical Framework.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-27

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Fairness, Accountability, and Transparency in AI-Based Decision Systems: A Systematic Review and Conceptual Framework

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