Transit AI influences service-frequency proposals across neighborhoods. Which trust control best addresses ethical performance?

Transit AI that influences service-frequency proposals can redistribute wait times and access across neighborhoods. Publishing a fairness dashboard makes disparate impacts visible to planners, boards, and the public so ethical performance is monitored continuously rather than asserted once in a briefing deck. Publishing a privacy dashboard for AI that influences service frequency proposals shows how rider data is protected, but it does not surface neighborhood-level fairness gaps in service-frequency proposals, so it cannot provide the public ethical accountability this question requires. Publishing an accuracy dashboard for AI that influences service frequency proposals reports overall prediction correctness, but it does not reveal whether AI-driven frequency proposals fairly distribute wait times and access across neighborhoods. Publishing a robustness dashboard for AI that influences service frequency proposals addresses model stability under stress and edge cases, but it does not supply the continuous, visible fairness oversight required for service-frequency proposals. Exam caveat: trust controls for civic AI that shapes public services include transparent, ongoing fairness measurement rather than one-time briefings or static slideware alone. Operational check: define neighborhood fairness metrics for frequency proposals, publish a maintained dashboard with a stated refresh cadence, review gaps in governance forums, and bind material disparity thresholds to redesign or mitigation actions before proposals advance to decision makers.