"I think this is fair'': Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
This work addresses the gap in understanding stakeholder perspectives for AI fairness governance, offering insights for more inclusive and nuanced fairness assessments.
The study investigated how non-expert stakeholders assess fairness in AI, specifically in a credit rating scenario, revealing that their decisions involve broader features, tailored metrics, and stricter thresholds than typical expert practices.
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 30 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments.