Pragmatic Fairness: Evaluating ML Fairness Within the Constraints of Industry

Machine learning (ML) fairness evaluation in real-world, industry settings presents unique challenges due to business-driven constraints that influence decision-making processes. While prior research has proposed fairness frameworks and evaluation methodologies, these approaches often focus on idealized conditions and may lack consideration for the practical realities faced by industry practitioners. To understand these practical realities, we conducted a semi-structured interview study with 21 experts from academia and industry specializing in ML fairness. Through this study, we explore three constraints of ML fairness evaluation in industry— balancing competing interests, lacking power/access, and getting buy-in—and how these constraints lead to satisficing, seeking satisfactory rather than ideal outcomes. We define the path from these constraints to satisficing as pragmatic fairness. Using recommender systems as a case study, we explore how practitioners navigate these constraints and highlight actionable strategies to improve fairness evaluations within these business-minded boundaries. This paper provides practical insights to guide fairness evaluations in industry while also showcasing how the FAccT community can better align research goals with the operational realities of practitioners.