Glossary
Updated
Encompasses technical robustness, fairness, transparency, privacy, and human oversight. Trustworthy AI programs define measurable criteria for each dimension (e.g., bias thresholds, uptime guarantees), embed them into development pipelines (impact assessments, audits), and track ethics KPIs in governance dashboards. Continuous stakeholder engagement, external audits, and published transparency reports reinforce trustworthiness over time.
Example
A government uses a trust-framework for its unemployment-benefits AI: it requires formal ethics reviews, publishes a transparency dashboard with model errors and biases, enforces human-in-the-loop for eligibility overrides, and commissions annual third-party audits, ensuring the system remains trustworthy to citizens and regulators.