Trust Calibration
A user deciding whether to use a government AI agent has little to go on. They may refuse one that would have served them well, or lean on one in a high-stakes case it is not equipped to handle. Calibrating that reliance takes evidence of how far this agent can be relied on for this decision. The agency that fields the agent is the only party positioned to supply it.
Officers inside the agency face the same shortage, with a duty attached that a user does not carry. An officer relying on an agent’s output is making an administrative decision, answerable and on the record. The evidence that lets a user calibrate is the evidence that lets an officer justify that decision, so publishing it serves both.
Policy challenge
Increased use of AI agents is happening faster than users have any settled basis for judging when to rely on one. The gap runs both ways: people withhold trust from an agent that would serve them well, and people lean on an agent in high-stakes interactions it cannot be trusted to handle. Automated systems run without calibrated trust or meaningful oversight have already produced serious public harm.
As deployment widens across services, the absence of a shared, legible basis for calibrating reliance becomes the condition users meet by default.
Design challenge
Let a user match their reliance on a government agent to the evidence for it.
Provide the means to read what an interaction involves and how much of it is automated.
For complex or high-stakes interactions, build a repeatable, transparent basis for trust.
Let delegation expand on demonstrated use and reverse the moment a user wants it gone.
Keep a path open for people who can't interpret the trust signals themselves.