Moving beyond Principles: Identifying Actionable AI Fairness Practices

TL;DR

This paper develops a structured set of actionable AI fairness practices across the AI lifecycle, addressing the gap between abstract principles and practical implementation to improve fairness governance.

Contribution

It introduces a comprehensive, role-specific matrix of AI fairness practices derived from discourse analysis, extending fairness from principles to actionable organizational guidance.

Findings

Derived a structured AI fairness practices matrix

Organized practices by obligation level and organizational role

Provides dynamic guidance for AI fairness implementation

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.