Synergistic barriers to algorithmic recourse in healthcare and administrative systems

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.