Data and code for: Label provenance in published artificial intelligence for tax and customs administration: a systematic review

Data, decisions and code behind the systematic review "Label provenance in published artificial intelligence for tax and customs administration: a systematic review" (Sushanta Paul, National Board of Revenue, Bangladesh). The review audits how published evaluations of AI in tax and customs administration obtained their ground-truth labels, how the labelled cases were selected, and whether the headline claims are supported by that evidence. The primary analysis covers 196 studies: 91 from the first search phase and 105 added by a later search. AI involvement. The first coding of every study was done by Claude. Every later step that the OSF registration (doi:10.17605/OSF.IO/2ANXY) assigned to humans was done by a panel of five language models from five vendors (openai/gpt-6-astra, google/gemini-4-argon, x-ai/grok-4.7, deepseek/deepseek-v4-pro-0813, qwen/qwen3.8-max-0902) under rules written before each run: title-and-abstract screening, the full-text eligibility check, blind adjudication of disputed codes, the second coder and the resolution of second-coder disagreements. No human took part in those steps, so the agreement figures for the main coding are agreement between language models. (Version 1.1.0 adds the one human check, the author's blind re-coding of 25 studies.) The protocol and its two addenda are in work/. Contents: search and screening records and decisions; eligibility decisions with quoted evidence; coding data with verbatim quotes and page numbers; the codebooks; screening sheets, model proposals and keys; the AI-completion protocol and addenda; adjudication, second-coder and resolution inputs and results; the panel's votes, parsed answers, run logs and manifests (prompt hashes, model identifiers, settings); analysis scripts, output tables and figures. README.md lists every folder and how to rerun the analysis. Not included: the full texts of the studies and the text extracted from them (copyright; each study is identified by DOI in the paper's supplementary material and in work/); the raw API response files, which stay with the author (the parsed answers, run logs and manifests are included); API keys. The files are in one zip archive; README.md lists the contents and how to rerun the analysis. Licences: data, documentation and figures CC BY 4.0; code MIT.