Structural limits of single-barrier reform in algorithmic recourse: A formal series-system model with implications for digital health
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 (
Fields of Study & Subjects
Ethics and Social Impacts of AI
Explainable Artificial Intelligence (XAI)
Digitalization
Law
and Regulation
Publication details
- Journal / source
- PLOS Digital Health
- Volume
- 5
- Issue
- 10
- Pages
- e0001784–e0001784
- Publication date
- 26 October 2026
- Language
- EN