What Medicine Taught Us About Fairness and What It Missed: Lessons from Reconsidering Race-Specific Lung Function Reference Algorithms

2026-05-22

AI Summary

This study addresses the long-standing neglect of algorithmic fairness theory in the revision of pulmonary function reference equations, which has led to biased modeling of racial differences. Through historical analysis, citation tracking, and quantitative modeling, it systematically examines the transition from race-specific GLI-2012 to race-averaged GLI-Global equations, revealing an implicit social determinist assumption that attributes 62% of the Black–White FEV₁ gap to environmental exposures. The work bridges clinical guideline development with algorithmic fairness theory, demonstrating that clinical practice adopted a “sufficiency” fairness criterion ahead of theoretical discourse, yet suffered from research inefficiencies due to overlooking impossibility theorems. These findings underscore the need for closer collaboration between medical and fairness communities to improve equity in clinical algorithms.

Abstract

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.

Problem

• algorithmic fairness
• race-specific algorithms
• lung function reference
• health equity
• clinical guidelines

Innovation

• algorithmic fairness
• race-specific algorithms
• lung function reference equations
• health equity
• GLI-Global