Trust Center
Responsible AI
Governance principles for AI-assisted products — oversight, privacy, evaluation, and risk-based controls.
AI policy topics
How we design, evaluate, and operate AI systems with appropriate human and technical safeguards.
Human oversight
High-impact AI outputs are reviewable by people. Escalation paths exist for sensitive decisions.
Privacy-first AI
Training and inference designs minimize personal data. Customer data is not used to train shared models without agreement.
Explainability principles
We prefer systems that can surface why a recommendation was made when the use case demands it.
Data governance
Datasets are classified, access-controlled, and documented for provenance and permitted use.
Model evaluation
Quality, bias, and safety evaluations are intended before production release and on material model changes when AI is in scope.
Risk management
AI risk is assessed by impact class — with stronger controls for automated decisions affecting people or finances.
AI policy FAQ
Training data use and how we reduce unsafe or ungrounded outputs.
Discuss AI governance
Talk with our team about responsible AI patterns for your product or platform.