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AI governance is the set of policies, regulations, and frameworks that guide the ethical development and deployment of AI systems. It covers everything from data handling practices to accountability structures and compliance requirements.
As AI becomes embedded in critical decisions, governance frameworks help organisations stay compliant with emerging regulations like the EU AI Act and build public trust.
The European Union's AI Act classifies AI systems by risk level and imposes strict requirements on high-risk applications such as biometric identification and critical infrastructure.
Responsible AI
Responsible AI is an approach to developing and deploying AI systems that prioritises fairness, transparency, accountability, and societal benefit. It encompasses practices and principles designed to minimise harm and ensure AI serves people equitably.
Transparency
Transparency in AI means being open about how a system works, what data it was trained on, its known limitations, and how decisions are made. It encompasses both technical transparency (model documentation) and organisational transparency (clear communication with stakeholders).
AI Safety
AI safety is a research field focused on ensuring that AI systems behave as intended and do not cause unintended harm. It encompasses technical challenges like robustness and reliability, as well as broader concerns about long-term risks from increasingly capable systems.
Our programme follows a structured Level 4 curriculum with project-based learning, practical workflows, and guided implementation across business and career use cases. Funded route available for UK citizens and ILR holders.