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Published (non-binding, current)Non-binding · Guidelines / framework

Data Quality Management Guidebook: Maximizing the Value of Data and AI

Record JP-09

Issued by
Japan AI Safety Institute (J-AISI)
Jurisdiction
Japan
Level · region
National · East Asia
Published / adopted
2025-03-31
Last updated
2026-05-14
Language
English

Summary

Guidebook of the Japan AI Safety Institute on managing the quality of data used to build and operate AI systems, on the premise that poor data quality degrades AI performance and trust. It treats data quality as one of the evaluation perspectives on AI safety, linked to fairness, robustness and verifiability, and organises international standards such as ISO/IEC 25012 and ISO/IEC 5259 into a framework of three views: a process view of the data life cycle from design to disposal, a governance cycle view, and a gateway view of data quality characteristics. It adds AI-specific points such as annotation quality, bias checks, drift management, synthetic data and monitoring.

Safety topics

evaluation & testingsafety institutetransparency & labelling

Scope

Borderline scope Existing scope_note kept.

Status notes

Version 1.00 was published on 31 Mar 2025, version 1.01 on 2 Dec 2025 and version 1.02 on 14 May 2026; version 1.02 turned the slide document into a text document and came with a machine-translated Japanese reference version and a data quality management checklist (version 1.00). The English text is the official edition. The guidebook was produced by J-AISI's standards team with data experts of IPA's Digital Infrastructure Center and J-AISI's Data Quality Sub-Working Group, and is maintained as a living document.

Sources

Open the official source https://aisi.go.jp/output/output_framework/data_quality_management_guidebook/

Local copy

Archived. Last attempt 2026-10-18 00:45:52 UTC

Official source (third-party copy, not published) archive/_runs/2026-10-14-discover-leads-2026-10-14/C014/2026-10-14/official.html
Retrieved 2026-10-14 13:11:03 UTC
SHA-256 3c3c16dbd816b94177d3a53e1186177c39e102bcf80714b1e2a08158c92d5e05
Linked document (PDF) (third-party copy, not published) archive/_runs/2026-10-14-discover-leads-2026-10-14/C014/2026-10-14/document-1.pdf
Retrieved 2026-10-14 13:11:14 UTC
SHA-256 10fe09c28d9dd0f40337d234c091515a012e066c309141d8e1b1aeb58c0c5789
Linked document (PDF) (third-party copy, not published) archive/_runs/2026-10-14-discover-leads-2026-10-14/C014/2026-10-14/document-2.pdf
Retrieved 2026-10-14 13:11:30 UTC
SHA-256 41acb993133c7b462af6ac9bfa4ed8d9d9c2a317dde995ee581a13cb1daabb26

Verification

A
The instrument's own official text, or an official register, was stored and confirms the title, date and status. checked 2026-10-18 · official-url-fetched
Recheck of 18 Oct 2026. Stored and read: J-AISI's bilingual release page on aisi.go.jp, the English guidebook version 1.02 (89 pages, 2026-05-14) and its Japanese reference translation (107 pages), all copies of 14 Oct 2026 that the archiver re-checked unchanged on 18 Oct 2026. The release page and the guidebook's version history confirm the title, the three version dates, the issuer and its contributors, that the guide is to be revised over time, and that the English version is the official edition and the Japanese one a reference translation; the summary and notes were checked against the English text and still hold.
Second check: Independent check by Claude Code (claude-sonnet-5-5), 2026-10-18: 6 field(s) supported, fixes applied to scope_note.

History

  1. 2026-10-18scope_note: Mainly a data management guidebook; included because the Japan AI Safety Institu → Existing scope_note kept. — pipeline run 2026-10-18-recheck-east-asia-1 (recheck), candidate JP-092026-10-18-recheck-east-asia-1 · Claude Code (claude-opus-5-5)
  2. 2026-10-14Added to the register — found in pipeline run 2026-10-14-discover-leads-2026-10-14, candidate C014 (JP/ai-bodies)2026-10-14-discover-leads-2026-10-14 · Claude Code (claude-opus-5-5)

Related records

Japan AISI Japan · Published (non-binding, current)

J-AISI Evaluation and Red Teaming Guides Japan · Published (non-binding, current)

Citation

Japan AI Safety Institute (J-AISI). Data Quality Management Guidebook: Maximizing the Value of Data and AI. 2025-03-31. https://aisi.go.jp/output/output_framework/data_quality_management_guidebook/. Status: Published (non-binding, current); verified 2026-10-18 (level A). Local copy archive/_runs/2026-10-14-discover-leads-2026-10-14/C014/2026-10-14/official.html retrieved 2026-10-14 13:11:03 UTC, SHA-256 3c3c16dbd816b94177d3a53e1186177c39e102bcf80714b1e2a08158c92d5e05. Every AI Policy Ever, version 1.18 (snapshot 2026-10-18), record JP-09.

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