Governance

Responsible AI Principles

Principles for designing, implementing and operating AI in ways that are useful, governable and proportionate to risk.

Purpose before deployment

Use AI where it supports a defined human, service or organisational outcome. Do not treat adoption as an objective by itself.

Accountability

Name accountable owners for the use case, data, controls, operation and decisions affected by the system. Responsibility remains with people and organisations.

Proportionate risk management

Assess potential harm, affected groups, uncertainty and consequence. Apply stronger evaluation, approval and human oversight where impact is higher.

Privacy, security and data stewardship

Use data lawfully and minimise exposure. Protect models, prompts, interfaces and outputs. Make provenance, permission and retention part of solution design.

Transparency and human agency

Help users understand when AI materially shapes an interaction or decision, what its limits are and how to question, correct or escalate an outcome.

Evaluation and continuous improvement

Test before release, monitor in context, document material changes and provide routes to pause or withdraw systems that do not meet the intended standard.