GOVERNANCE / RESPONSIBLE AI
Responsible AI
Principles for developing and applying intelligent systems with care, control, and evidence.
Purpose and proportionality
We begin with the problem and assess whether AI is an appropriate method for the environment and consequence involved.
Human oversight
Systems should preserve meaningful human review, escalation, and control where decisions or actions require it.
Boundaries and permissions
Agentic and automated systems should operate within explicit permissions, safeguards, and defined stopping conditions.
Traceability
Data, model, and system behaviour should be measurable so that failure, drift, and unexpected outcomes can be investigated.
Information quality
Reliable AI depends on data quality, provenance, security, and appropriate access controls.
Continuous learning
Deployment observations should inform testing, engineering improvements, and future research—without hiding uncertainty or limitations.