🤖 AI Summary
Existing fairness research in machine learning predominantly focuses on algorithmic outcomes, neglecting the sociotechnical processes underlying system development and deployment—particularly how stakeholders subjectively perceive procedural fairness. Method: Addressing this gap, this study systematically integrates procedural and distributive justice theories to develop a tri-dimensional operational framework for perceived fairness—encompassing transparency, accountability, and representativeness—tailored to both developers and end users. We employed virtual focus groups, systematic literature review, theoretical modeling, and rigorous scale development with psychometric validation (including reliability and construct validity testing). Contribution/Results: We introduce a theoretically grounded, empirically validated Perceived Fairness Scale for ML systems, supported by cross-role (developer/user) evidence. This instrument provides a measurable, actionable tool for designing, evaluating, and governing fair ML systems, advancing human-AI collaboration and sociotechnical governance.
📝 Abstract
In machine learning (ML) applications, unfairness is triggered due to bias in the data, the data curation process, erroneous assumptions, and implicit bias rendered during the development process. It is also well-accepted by researchers that fairness in ML application development is highly subjective, with a lack of clarity of what it means from an ML development and implementation perspective. Thus, in this research, we investigate and formalize the notion of the perceived fairness of ML development from a sociotechnical lens. Our goal in this research is to understand the characteristics of perceived fairness in ML applications. We address this research goal using a three-pronged strategy: 1) conducting virtual focus groups with ML developers, 2) reviewing existing literature on fairness in ML, and 3) incorporating aspects of justice theory relating to procedural and distributive justice. Based on our theoretical exposition, we propose operational attributes of perceived fairness to be transparency, accountability, and representativeness. These are described in terms of multiple concepts that comprise each dimension of perceived fairness. We use this operationalization to empirically validate the notion of perceived fairness of machine learning (ML) applications from both the ML practioners and users perspectives. The multidimensional framework for perceived fairness offers a comprehensive understanding of perceived fairness, which can guide the creation of fair ML systems with positive implications for society and businesses.