Synergistic barriers to algorithmic recourse in healthcare and administrative systems

Abstract

Abstract Algorithmic decision systems mediate access to healthcare, credit, employment and housing, yet individuals who experience adverse decisions face multi-stage barriers when seeking recourse. We formalize these barriers as a series-structured system with 11 empirically parameterized stages across three layers (data integration, data accuracy and institutional access) and prove that single-barrier interventions are bounded by baseline system success. Under baseline parameterization derived from federal datasets and peer-reviewed algorithmic audit studies, end-to-end recourse probability is 0.0018%. Removing any single barrier yields negligible improvement (<0.02%). Factorial decomposition reveals that the three-way cross-layer interaction accounts for 87.6% of achievable improvement, confirmed by Shapley attribution, Sobol sensitivity analysis and bootstrap resampling ( n = 1,000 ). These results provide a structural explanation for the limited impact of incremental reforms and support coordinated multi-layer intervention approaches for clinical AI governance and algorithmic fairness.

Ähnliche Arbeiten