Translating public perception pathways into responsible local government AI practice
| Pathway | Empirical basis | Implication for local governments |
|---|---|---|
| Experience-building through visible urban services | Experience with local government AI strengthens understanding and trust | Introduce AI through visible, service-oriented applications where residents can observe practical value, such as traffic management, service scheduling, infrastructure maintenance, or administrative support |
| Public value communication | Trust and urban-AI attitudes are associated with perceived benefits | Explain why AI is being used, what public value it is expected to provide, what data are involved, and how service outcomes will be monitored |
| Safeguards for sensitive applications | Urban-AI attitudes are associated with openness to surveillance-AI, but surveillance remains ethically contested | Treat extensions to surveillance or high-stakes applications as requiring stronger transparency, privacy protection, human oversight, contestability, and public justification |
| Context-sensitive governance | Cross-context patterns indicate different relationships among trust, risk, and responsible-AI prioritisation | Adapt communication and governance strategies to observed public concerns, including risk sensitivity, trust formation, and expectations for fairness, accountability, security, and reliability |
| Ongoing review and public learning | Responsible-AI expectations are shaped by risk perception and application-specific attitudes | Monitor public feedback, risk perceptions, unintended consequences, and service outcomes, and revise implementation practices over time |
| Pathway | Empirical basis | Implication for local governments |
|---|---|---|
| Experience-building through visible urban services | Experience with local government AI strengthens understanding and trust | Introduce AI through visible, service-oriented applications where residents can observe practical value, such as traffic management, service scheduling, infrastructure maintenance, or administrative support |
| Public value communication | Trust and urban-AI attitudes are associated with perceived benefits | Explain why AI is being used, what public value it is expected to provide, what data are involved, and how service outcomes will be monitored |
| Safeguards for sensitive applications | Urban-AI attitudes are associated with openness to surveillance-AI, but surveillance remains ethically contested | Treat extensions to surveillance or high-stakes applications as requiring stronger transparency, privacy protection, human oversight, contestability, and public justification |
| Context-sensitive governance | Cross-context patterns indicate different relationships among trust, risk, and responsible-AI prioritisation | Adapt communication and governance strategies to observed public concerns, including risk sensitivity, trust formation, and expectations for fairness, accountability, security, and reliability |
| Ongoing review and public learning | Responsible-AI expectations are shaped by risk perception and application-specific attitudes | Monitor public feedback, risk perceptions, unintended consequences, and service outcomes, and revise implementation practices over time |
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