Fairness in histopathology AI — systematic review: extraction, audit, and verification artifacts (79-study corpus, manuscript v0.3.0)

Machine-readable evidence and analysis code for a PRISMA-2020 systematic review of fairness in histopathology AI. Contents. (i) The dual-coded, adjudicated extraction table for the 62-study analytical corpus, with the raw multi-coder outputs and inter-coder agreement statistics; (ii) the screening, de-duplication and post-hoc eligibility-audit logs; (iii) per-coder and adjudicated QUADAS-2 risk-of-bias judgements for all 62 studies; (iv) the full consensus-endorsement coding pipeline — screened review pools, the pre-specified random sample (n=30, seed 20260913), the coding specification, two independent coder passes, disagreements and their adjudication, and tallies with Wilson 95% confidence intervals; (v) the 39-dataset demographic-metadata audit; and (vi) a claim-by-claim statistics register mapping every reported number to its source rows, together with the code that produces each value. Full texts of the reviewed papers are not included, for copyright reasons. Code is licensed MIT; data tables are licensed CC BY 4.0. A SHA-256 manifest (MANIFEST.sha256) covers every file in the package. This package is a working-tree snapshot of the manuscript as of 30 September 2026 (45 pp main + 27 pp supplementary). Version of 30 September 2026. Adds the 79-study update: the dual-coded extraction and risk-of-bias tables including the 17 added studies, the post-hoc coding directory, a disposition for each of the 114 advanced screening records, the resolution of five full-text candidates, and a sensitivity analysis with one internally inconsistent study removed. Full texts of the reviewed papers are not included. Manuscript v0.3.0 (30 September 2026). The 79-study tables are unchanged. This version records that the manuscript now uses those tables in the discussion and the PRISMA checklist: author geography 81.0% (64/79), East Asia 12 studies, pediatric cancer the primary focus of two studies; any fairness metric 27.8% (22/79), expected calibration error in three studies; overall risk of bias 9% low (7/79) and 72% unclear (57/79). Full texts of the reviewed papers are not included.