Persistent Proxy Discrimination in HIV Testing Prediction Models: A National Fairness Audit of 386,775 US Adults

BackgroundIn clinical contexts where disease burden differs across demographic groups, enforcing demographic parity -- equal prediction rates regardless of group -- may reduce screening for the populations that need it most. We demonstrate this using HIV testing prediction as a case study. MethodsUsing the Behavioral Risk Factor Surveillance System (BRFSS) 2024 dataset (N=386,775), we trained four classifiers to predict HIV testing uptake and evaluated disparities using demographic parity difference (DPD), equalized odds difference (EOD), and calibration across eight racial/ethnic groups. We applied threshold optimization and exponentiated gradient mitigation and quantified their impact on high-burden populations, including intersectional effects across race and sex. ResultsBaseline selection rates ranged from 12.1% (Asian) to 66.0% (Black), mirroring differential HIV burden (DPD 0.519-0.634). Race-blind models retained 70% of baseline disparity through correlated social determinants. Enforcing demographic parity reduced Black true positive rates from 78.2% to 30.0% (61.6% relative decrease), causing 1,610 additional missed individuals. Race-only optimization worsened sex-based disparity by 71%; multi-objective optimization reduced intersectional DPD from 0.609 to 0.076 but at the same cost to high-burden groups. Exponentiated gradient AUC fell from 0.671 to 0.592 (11.8% relative decrease). Survey-weighted sensitivity analysis confirmed unweighted estimates underestimated disparities. ConclusionsDemographic parity is an inappropriate fairness criterion in differential-burden clinical contexts because it reduces screening access for high-risk populations. Fairness audits in healthcare should use need-appropriate metrics (equalized odds, calibration) rather than defaulting to demographic parity, and metric selection should involve clinician and community stakeholder deliberation.