{
  "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, illustrated by Shapley attribution, Sobol sensitivity analysis and bootstrap resampling (n = 1,000) within prespecified perturbation ranges. Introducing dependence among the stage pass-events with a Gaussian-copula analysis (latent correlation up to ρ = 0.5), while preserving each marginal pass probability, leaves the comparative conclusion—single-layer reform yields far less than coordinated reform—intact, while a repeated-attempt topology attenuates it, delimiting the model’s domain of validity. These findings are properties of the modeled system rather than empirical measurements of real recourse pathways; they yield testable hypotheses for digital-health governance—chiefly that single-layer fairness interventions are structurally unlikely to produce large absolute gains—pending validation against observed recourse data. Author summary: When an algorithm makes an adverse decision about you—denying a loan, filtering your job application, or miscategorizing your health risk—what does it take to correct it? We built a formal mathematical model of the 11 barriers a person must clear to obtain recourse, from detecting the error to accessing legal resources, and studied its structure. Calibrated with data from federal agencies and one healthcare audit—a provisional, cross-domain calibration—the model puts the probability of clearing all barriers below 2 in 100,000, and in the model no single-barrier fix raises it by more than 0.02%. This follows from the model’s multiplicative structure: barriers compound rather than add. Within the model, the three-way interaction across data integration, data accuracy and institutional access accounts for 87.6% of the achievable improvement. These are properties of the model rather than measurements of real recourse; they generate a testable hypothesis—that coordinated, multi-layer reform may outperform isolated single-layer fixes—which remains to be validated against observed recourse data.",
  "abstract_shown": true,
  "affiliations": [
    "Nyx Dynamics LLC, Philadelphia, Pennsylvania, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
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        0
      ],
      "name": "A. C. Demidont"
    }
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  "bibtex": "https://research.draconitic.com/paper/ba0qcgn41k4wh.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001784",
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  "pmcid": "PMC13705585",
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  "published": "2026-10-01",
  "publisher": "Public Library of Science",
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    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pdig.0001784",
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  "title": "Structural limits of single-barrier reform in algorithmic recourse: A formal series-system model with implications for digital health",
  "title_text": "Structural limits of single-barrier reform in algorithmic recourse: A formal series-system model with implications for digital health",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/ba0qcgn41k4wh",
  "venue": "PLOS Digital Health",
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