[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fai-in-education-shared-models-for-learning-assessment-and-privacy":3},"---\ntitle: Shared Models for Learning, Assessment and Privacy\ndescription:  When AI governance lives in different places.\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - ai\n  - education\n  - mlops\n  - privacy\n---\nWhen AI governance lives in different places for each team, control failures multiply faster than deployments.\n\n## Why this matters\n\nWithout shared meaning, each team rebuilds governance controls separately—multiplying risk.\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\nWhen AI governance is semantically consistent, teams move fast without creating control conflicts.\n",1776235584168]