Fairness in histopathology AI — systematic review: extraction, audit, and verification artifacts (62-study corpus)

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 matching the submitted manuscript (36 pp main + 16 pp supplementary).