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This page documents how AI Regulation Watch collects, transforms, verifies, and publishes regulatory information. The workflow is built to preserve provenance, reduce ambiguity, and keep the public view focused on clear and attributable statements.
The production pipeline uses six explicit stages. Each stage has file-based outputs and quality checkpoints so we can audit changes and rerun steps without losing traceability.
Collect source-native records from tracked streams and store reproducible snapshots.
Standardize URLs, timestamps, and taxonomy mappings to form deterministic canonical records.
Generate short plain-language summaries from source material without copying third-party tracker text.
Merge matched records, deduplicate by source URL, and prepare publication candidates.
Apply human editorial checks for factual coherence, status integrity, and source attribution.
Expose only approved public entries with translation status and publish-state controls.
Harvest captures source-native records and stores snapshots for reproducibility. Normalize applies canonical URL handling and structure mapping so the same source document is represented consistently over time. Rewrite converts source content into concise public-facing summaries in plain language while avoiding third-party wording reuse. Compile merges records across source streams and resolves duplicates around canonical URLs.
Review is a human-controlled gate. Editors verify factual coherence, status framing, and category placement. Publish then exposes only records that satisfy publication controls, including translation approval constraints. This separation prevents accidental leakage of draft or machine-only content to public routes.
Quality is managed through layered controls rather than a single score. Cross-source verification compares equivalent records when multiple streams reference the same primary source. Confidence scoring reflects evidence density, recency, and consistency. Conflict flagging marks unresolved disagreements between status claims, date signals, or category mappings.
We also run stale detection to identify entries that have not been confirmed recently within their expected update cadence. Staleness does not automatically imply inaccuracy, but it triggers review priority because policy environments can shift through amendments, delegated acts, and enforcement guidance.
Provenance fields support these controls by preserving who asserted sensitive attributes and when they were observed. This matters when two respectable sources disagree or when a tracker trails an official register.
The source system is deliberately source-native first. We do not erase original sectioning at ingest. Instead, each source contributes its own section labels and categories, which are retained and then mapped into our derived taxonomy for public filtering. This lets us evolve mapping logic without destroying historical context.
Yellow stream emphasizes early policy and regulator communications. It is useful for momentum signals and agenda direction but may include preliminary language. Green stream prioritizes formal legal process and official texts where available. Blue stream focuses on supranational and international governance institutions whose outputs can shape national implementation choices.
We collect legal acts, bills, consultations, soft-law guidance, sandbox announcements, and selected governance-relevant research. We do not attempt to ingest every AI news item on the internet. The objective is targeted regulatory intelligence, not comprehensive media monitoring.
High: Confirmed by a strong primary source path or multiple aligned sources, recently reviewed, and free from unresolved material conflicts.
Medium: Sufficient evidence for directional use, but with partial corroboration, older verification windows, or minor unresolved ambiguity.
Low: Limited corroboration, significant source conflict, or structural uncertainty in status/category mapping. These items are visible with caution and prioritized for follow-up.
Coverage depth varies across jurisdictions. Some countries publish machine-readable legal data with robust metadata, while others distribute updates through fragmented channels with inconsistent language and delayed archival behavior. This creates uneven confidence and update lag across regions.
Language is another constraint. Public summaries are optimized for clarity in English first, with translation workflows layered on top. Translation quality is managed, but legal nuance can still depend on primary-language interpretation in official texts.
Finally, status transitions can be unclear in early phases of a policy cycle. In those cases, we avoid inferred certainty and keep status conservative until stronger evidence appears.
| Tier | Typical scope | Target refresh |
|---|---|---|
| S | High-volume jurisdictions and major supranational bodies | Daily monitoring, weekly publish cycle |
| A | Active policy jurisdictions with frequent draft activity | 2-3 checks per week |
| B | Moderate update environments | Weekly checks |
| C | Low-frequency jurisdictions or sparse source ecosystems | Biweekly to monthly checks |
Compliance officers can use country and section views to monitor emerging obligations, build issue lists for counsel, and prioritize internal control reviews. Researchers can trace policy diffusion and compare legal framing across regions. Journalists can use summaries as orientation, then verify claims through linked source documents before publication.
For corrections or missing materials, contact hello@caesar.no.