[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fhousing-ontologies-for-fair-access-eligibility-and-property-decisions":3},"---\ntitle: Housing Ontologies for Fair Access, Eligibility, and Property Decisions\ndescription: Structured ontology semantics for Fair Access, Eligibility, and Property Decisions\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - ai\n  - housing\n---\nWhen AI governance lives in different places for each team, control failures multiply faster than deployments.\n\n## Why this matters\n\nShared semantics prevent teams from building conflicting AI policies that undermine each other.\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\nShared AI semantics turn governance from a bottleneck into a capability.\n",1776235587107]