Responsible AI
Principles that guide how we scope, engineer, evaluate, and operate AI systems.
Last updated: 27 July 2026
Purpose and accountability
We begin with a defined human or business outcome, named owners, and clear limits on what an AI system should decide or automate.
Privacy and security
We minimize sensitive data, establish access boundaries, review model and vendor data handling, and apply controls proportionate to the use case.
Evaluation and transparency
Systems are evaluated against relevant quality, safety, bias, grounding, and reliability criteria. Material limitations are documented for operators and decision-makers.
Human oversight
Higher-impact workflows require meaningful review, escalation paths, monitoring, and the ability to override or suspend automated behavior.
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