[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fsmart-cities-ontologies-for-urban-ai-mobility-and-public-safeguards":3},"---\ntitle: Smart Cities Ontologies for Urban AI, Mobility, and Public Safeguards\ndescription: Structured ontology semantics for Urban AI, Mobility, and Public Safeguards\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - ai\n  - safety\n  - smart-cities\n---\nShared AI governance only works when all teams interpret risk, fairness, and control the same way.\n\n## Why this matters\n\nWhen concepts are defined consistently, AI behavior becomes predictable and auditable across systems.\n\n## What this looks like in practice\n\n- An auditor traces AI governance decisions from policy to code to test results without guessing.\n- Different teams use the same terms to mean the same thing, even when implementing differently.\n- Risk classifications are consistent whether assessed by humans, tools, or external regulators.\n\n## How teams use it\n\n- defining bias and fairness in ways that survive across ML frameworks and deployment contexts\n- aligning audit trails so compliance evidence is usable by multiple teams without rewriting\n- connecting model governance to software supply chain controls\n\nAI safety is a team property—it depends on shared interpretation across all humans and systems.\n",1776235589407]