Trust Calibration
Trust in an agent can miss in either direction. A citizen might not trust using one that would have served them well, or lean on one in a high-stakes case it is not equipped to handle. Both sides need a basis for judging whether an agent’s output can be relied on for the decision.
A citizen needs something legible to judge an agent by, and an agency needs to supply a dependable basis for that judgment. The same miscalibration runs inside government, where an officer can over-rely on an agent’s output or distrust one that would have caught the error.
Increased use of AI agents is happening faster than citizens 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 actually 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 citizens meet by default.
Let a citizen 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 citizen wants it gone.
Keep a path open for people who can't interpret the trust signals themselves.