urn:uuid:9b09c31b-101f-50a5-b1c4-72d46479b655SocioLens — LD-05: Discretion curtailed, relocated or encoded?New versions of this debate, revisions of its claims, Radar updates and research opportunities.2026-10-21T00:00:00ZSocioLensurn:uuid:a722cf9b-de44-5a9b-a994-4b1e80bf0107Claim LD05-HQ-C04 revised to v1.12026-10-21T00:00:00ZWhat changed: The corrected disparity figures are no longer held back. The main result now gives them from the authors’ corrected analysis: for discretionary referrals in 2016–18, the gap between the screen-in rates of Black and white children was 17.9 percentage points for AFST-only (counterfactual) decisions (67.9% against 50.0%) and 7.1 points for decisions by workers using the AFST (50.2% against 43.1%). The public-facing claim now leads with the disparity result that the correction supports, and gives the 21.0% override share second, as a figure of the original analysis whose numbers the authors later said all changed. The limitations add that the corrected analysis is a preprint, that its authors did not report the override share again, and that the study period and population of the 21.0% share are not recorded here. The direction of the claim, its sources, scope and verification state are unchanged.
Why: Version 1.0 held back the exact 17.9→7.1 figure (marked HOLD in the R04 LD-05 Canonical Claim Ledger v1.0), pending direct extraction from the corrected analysis. On 21 October 2026 the full text of Stapleton et al., arXiv:2204.13872v1 (29 April 2022), was read line by line. Appendix A, "Detailed Comparisons of Analyses 1 and 2", first point, Analysis 2 column, reports that AFST-only decisions would have screened in 67.9% of Black and 50.0% of white children in discretionary referrals from 2016-18, "a racial disparity of 17.9%", and that workers using the AFST screened in 50.2% and 43.1%, "a disparity of 7.1%". The paper’s own "%" is a difference between two rates, so this version calls it percentage points. The check read a text extraction of the PDF and looked at its figure pages. The paper’s Figure 1(b) plots the same screen-in rates; it labels the AFST-only gap 17.9% but the worker+AFST gap 7.0%, 0.1 point below the 7.1% of Appendix A. This version follows the appendix text and its rates, whose difference is 7.1 points. Section 2 of the same paper says that "all of the specific numbers we reported in Analysis 1 changed in Analysis 2"; the HQ batch notes take the 21.0% override share from the original CHI 2022 paper, which reports Analysis 1, and the corrected analysis does not report that share again. The CHI 2022 paper itself was not read for this revision (the ACM Digital Library blocks the project’s tools), so the attribution of the 21.0% share to the original analysis rests on the HQ batch notes. The check and this revision were done by AI agents reading the paper, not by a person.urn:uuid:daa9dde0-956d-5aff-9995-e4049d387f79New research opportunity: RO-LD03-07 — Is the time AI saves paid for with weaker checking?2026-10-16T00:00:00ZWhere public services and clinics turn the time saved by generative-AI drafting into higher caseloads (social-work case records, clinical notes), does the time spent checking each output fall and the share of errors that survive into the signed record rise, and how much of any rise comes from time pressure rather than from a decline in the checkers' own skill?urn:uuid:eb17ffd1-83ef-5a54-a385-e4ac57c9d641Claim LD05-HQ-C05 revised to v1.12026-10-13T00:00:00ZWhat changed: The public-facing claim now says that the racial-disparity result comes from working-paper evidence ("separate working-paper evidence" instead of "separate evidence"). The Kentucky erosion result and both sources are unchanged.
Why: A claim check on 13 October 2026 found that version 1.0 dropped the working-paper qualifier that the HQ cross-batch synthesis cleared for this claim ("separate working-paper evidence"); the HQ notes say the 2019 Albright paper is a working paper and must not be given the same maturity as Stevenson 2018. The check and this revision were done by AI agents against the HQ notes, not by a person.urn:uuid:123f967b-fccb-59cf-b26a-5c603929a0c6Claim LD05-HQ-C14 revised to v1.12026-10-13T00:00:00ZWhat changed: The public-facing claim now names the one decision HQ verified, the Federal Administrative Court (BVwG) decision of 1 September 2025, instead of "Austrian courts", and the source field cites only that decision, dropping "prior VwGH/BVwG procedural history". The legal finding and the effective-discretion boundary are unchanged.
Why: A claim check on 13 October 2026 found that the HQ notes verify a single decision (BVwG W256 2235360-1, 1 September 2025); HQ recorded the earlier VwGH/BVwG procedural history as part of the legal record but did not verify its content, so the plural "courts" claimed more than the record shows. Field 6 now cites only the verified decision; the same Source record is still cited. The check and this revision were done by AI agents against the HQ notes, not by a person.urn:uuid:adb2cab7-edfe-51ee-9c1c-6d159da8058bResearch opportunity revised to v2: RO-LD05-01 — Outside corrections, how often do officials depart from algorithmic recommendations?2026-10-13T00:00:00ZNarrowed after the literature check. The claim that no cross-system compilation of override rates exists is too broad. In offender risk assessment there is an established override literature that reports override rates and direction across tools and jurisdictions, now synthesised in a 2025 scoping review and meta-analyses of 21 studies covering eight tools (Psychology, Public Policy, and Law 31(3), doi:10.1037/law0000448), and single studies there report rates from about 7% to 74% with upward and downward overrides coded separately. No compilation was found for benefits, employment services, child welfare, pretrial or immigration systems, or across those sectors under one definition, so the opportunity should be narrowed to those sectors and use the correctional literature as a benchmark and coding template. The verdict changed from "No study found doing the core design" to "Related work exists".urn:uuid:a370c274-833f-5150-987e-3a9b80225438Research opportunity revised to v2: RO-LD05-02 — Does telling reviewers a case was picked by a risk model change what they decide?2026-10-13T00:00:00ZLiterature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.urn:uuid:2c7e8f5d-9f30-503c-936a-5e6a0584f4c8Research opportunity revised to v2: RO-LD05-03 — What does being flagged by a fraud-risk model cost claimants before any decision?2026-10-13T00:00:00ZLiterature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.urn:uuid:a9301b5c-62b3-5868-99a7-f4366f296149Research opportunity revised to v2: RO-LD05-04 — How many AI transcription errors survive social workers' checks into case records?2026-10-13T00:00:00ZLiterature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.urn:uuid:b5caea99-8ad0-5b23-94d6-16c5df7477c8Research opportunity revised to v2: RO-LD05-05 — In contracts for public decision systems, who controls thresholds, variables and updates?2026-10-13T00:00:00ZNarrowed after the literature check. A systematic review of publicly available US state-level AI procurement contracts now exists (Cusenza, SSRN 6812838, forthcoming Drexel Law Review), covering data access, intellectual property, stakeholder engagement, testing and cybersecurity provisions and finding that contracts rarely address algorithmic-governance risks. That covers part of the proposed contract analysis, but on the abstract it is US-only, state-level, not limited to decision-support systems, and does not code authority over thresholds, input variables, retraining or updates, nor compare vendor-built with in-house systems. Other new work codes vendor FactSheets (Kuehnert et al., 2026) or model clauses and guidance (Abdulbari, Frontiers 2026), not executed contracts. The opportunity should be narrowed to decision authority in contracts for decision-support systems, cross-nationally.urn:uuid:86dff863-75e8-54c2-8aec-02ad3dc17868Research opportunity revised to v2: RO-LD05-06 — Who first detects failures in public-sector algorithmic systems, and how late?2026-10-13T00:00:00ZLiterature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.urn:uuid:941c983e-f44f-5930-bd00-a6127f3c596eNew research opportunity: RO-LD05-01 — How often do officials depart from algorithmic recommendations, across systems?2026-10-12T00:00:00ZAcross deployed public-sector risk and profiling systems, how often do officials formally or informally depart from the system's recommendation, in which direction, and how much of the variation between systems survives once override is defined and counted the same way?urn:uuid:124d1317-4231-557c-9cb5-c11ad6b75bd5New research opportunity: RO-LD05-02 — Does telling reviewers a case was picked by a risk model change what they decide?2026-10-12T00:00:00ZWhen public officials review a case selected for extra scrutiny, does knowing that a risk model selected it, or seeing its risk score, change how often they decline the claim, how hard they look, and whether they catch errors planted upstream in the case file, and do these effects differ by the claimant's nationality?urn:uuid:971050e4-36c8-53bf-bb8b-f815a278fa50New research opportunity: RO-LD05-03 — What does being flagged by a fraud-risk model cost claimants before any decision?2026-10-12T00:00:00ZFor benefit claimants selected for extra checks by a risk model, how large are the burdens imposed before any formal decision (payment delay, evidence requests, withdrawn or abandoned claims, repeat applications), how do they compare with claimants selected at random or not selected, and how are they distributed by nationality and age?urn:uuid:2f6b8457-29bb-5bf8-b513-7d4705e36303New research opportunity: RO-LD05-04 — How many AI transcription errors survive social workers' checks into case records?2026-10-12T00:00:00ZIn local authorities using generative-AI transcription and summarisation in social work, what share of final, signed case records contain errors (invented content, omissions, changed meaning) that are absent from the recorded conversation, which kinds survive the practitioner's check, and what happens when people who draw on care read and try to correct them?urn:uuid:3b5a51af-e631-58a7-9b22-5a9123424112New research opportunity: RO-LD05-05 — Who holds decision authority in public-sector algorithm contracts, agency or vendor?2026-10-12T00:00:00ZIn the contracts public agencies sign for risk-scoring, profiling and case-routing systems, who is given authority over the choices that shape decisions (thresholds, input variables, retraining and updates, performance targets), what audit and documentation rights does the agency keep, and does this differ between systems built in-house and those bought from vendors?urn:uuid:34a8a27e-adb9-5dd9-adb5-516a49fc8490New research opportunity: RO-LD05-06 — Who first detects failures in public-sector algorithmic systems, and how late?2026-10-12T00:00:00ZFor documented failures of public-sector algorithmic systems since 2020, who first detected the problem (internal monitoring, frontline staff, auditors, regulators, courts, journalists, advocacy groups, affected people), how long after deployment, and did detection lead to one-case correction or to system-level redesign?urn:uuid:84be7910-a30f-5542-a481-b6309f4dc31dNew debate: LD-05 v0.1 — Discretion curtailed, relocated or encoded?2026-09-25T00:00:00ZWhen public organisations introduce algorithmic, machine-learning or foundation-model systems into administrative decision-making, does human discretion actually shrink?