Moving beyond Principles: Identifying Actionable AI Fairness Practices
For organizations implementing AI, this provides a structured, role-specific scaffold to operationalize fairness, moving beyond abstract principles.
The study addresses the principles-to-practice gap in AI fairness governance by developing actionable practices through discourse and thematic analyses of 60 sources, resulting in a lifecycle-spanning matrix organized by obligation degree and organizational role.
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.