What Medicine Taught Us About Fairness and What It Missed: Lessons from Reconsidering Race-Specific Lung Function Reference Algorithms
For researchers and practitioners in algorithmic fairness and clinical medicine, this work highlights the need for deeper engagement between communities to improve equity in healthcare algorithms.
The paper analyzes the transition from race-specific to race-averaged lung function reference algorithms, finding limited cross-citation between fairness and clinical communities, implicit encoding of social determinants (62% of Black-White FEV1 gap attributed to exposure), and that clinical validation used a sufficiency-like fairness criterion before formalization, while neglecting impossibility results.
Since 2019, medical societies have reconsidered race-specific clinical equations often in parallel to and largely independent from algorithmic fairness research. Focusing on lung function reference algorithms that affect medical care, insurance, and employment for hundreds of millions globally, we analyze the transition from race-specific GLI-2012 to race-averaged GLI-Global through a fairness lens. Drawing on historical context, citation analysis, and quantitative evaluation, we show (i) limited cross-citation between FAccT and clinical guideline revision efforts; (ii) that GLI-Global implicitly encodes assumptions about social determinants of health, behaving as if ~62% of the Black-White gap in FEV1 is exposure-related; and (iii) clinical validation studies operationalized a sufficiency-like fairness criterion long before its formalization in fairness literature, while neglecting foundational results such as the impossibility theorem has led to inefficiencies in clinical research. Overall, our analysis highlights the value of deeper, mutually beneficial engagement between medical and fairness communities and the public to accelerate progress toward equitable healthcare algorithms.