Structural limits of single-barrier reform in algorithmic recourse: a formal series-system model with implications for digital health

Single-barrier reforms in algorithmic recourse systems achieve less than 0.02% improvement, with cross-layer interactions accounting for 87.6% of potential gains in digital health contexts.

• Why it matters: Understanding the structural limitations of individual interventions is crucial for designing effective policies that improve access to healthcare and other essential services mediated by algorithms.
• What they did: The study modeled a series-structured system with 11 empirically parameterized stages across three layers, analyzing the impact of single and coordinated reforms using sensitivity analyses, Gaussian-copula dependence, and bootstrap resampling.
• The result: Findings suggest that single-layer fairness interventions are inherently limited, emphasizing the need for coordinated, multi-layer reforms to significantly enhance algorithmic recourse and digital health equity.