Track AI Policy in the Federal Register

For AI policy, search both "artificial intelligence" and narrower terms such as "automated decision systems" or "machine learning." Split final rules from proposed rules so an alert does not imply that a proposal is already binding.

Use the Federal Register Scraper to make that search reproducible. Save the exact filters and result cap as a Task rather than editing a one-off query every time.

Turn the search into a decision

Route by agency and document type. A proposed rule with an open comment deadline belongs in a policy workflow; a final rule with an effective date belongs in compliance planning.

Create explicit inclusion columns such as is_in_scope, owner, and review_reason. Keep them in a derived table, not in the raw source rows. This makes it possible to rerun qualification rules without losing the original evidence.

Sample ten included and ten excluded records. If exclusions are caused by missing upstream fields, do not silently treat those fields as false. Put them in a review queue or document the assumption.

Refresh without creating duplicates

The record key is document_number. Upsert on that key and record the collection timestamp separately. A recurring workflow should distinguish a newly published record from an older record whose metadata changed.

A search result is not a legal conclusion. Proposed rules, final rules, and notices have different effects. Preserve document type and official links so counsel or policy owners can review the actual publication.

The practical payoff is a smaller, explainable queue. The source query finds candidates; the qualification layer decides which candidates deserve attention. Mixing those two jobs into one opaque keyword search makes both harder to audit.