Moving beyond Principles: Identifying Actionable AI Fairness Practices

2026-04-20

AI Summary

This study addresses the persistent gap between abstract ethical principles and concrete implementation in AI fairness governance, which currently lacks actionable, lifecycle-spanning guidance. Drawing on sociotechnical and practice-based perspectives, the research synthesizes 60 academic, policy, and practitioner-oriented documents and employs discourse and thematic analysis to develop the first structured, role-oriented AI fairness practice matrix. This matrix offers modular and dynamic governance guidance tailored to organizational roles and their corresponding levels of obligation, spanning the entire AI system lifecycle. By aligning responsibilities with practical actions across development, deployment, and monitoring phases, the framework provides organizations with a systematic foundation for implementing feasible and sustainable fairness governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Because artificial intelligence (AI) increasingly mediates organizational work, fairness has become a critical governance challenge. Existing frameworks often prioritize abstract ethical principles rather than fairness-specific ones and lack actionable guidance across the entire AI lifecycle. This study addresses the principles-to-practice gap in AI fairness governance. We develop actionable AI fairness practices and draw on a socio-technical and praxiological lens, conducting discourse and thematic analyses of 60 academic, policy, and practitioner sources. From these analyses, we derive a structured set of AI fairness practices in a comprehensive, AI lifecycle-spanning matrix organized by obligation degree and organizational role. The matrix provides dynamic, role-specific guidance to support implementation and sustainment of AI fairness. By extending the AI fairness beyond abstract principles to operationalized, actionable practices, we contribute to IS scholarship and offer a modular governance scaffold.

Problem

• AI fairness
• principles-to-practice gap
• actionable practices
• AI lifecycle
• governance

Innovation

• actionable AI fairness
• AI lifecycle
• socio-technical governance
• praxiological approach
• fairness implementation