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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).
Transparency builds trust with users, regulators, and the public — and is increasingly required by AI governance frameworks and regulations worldwide.
Anthropic publishes detailed model cards for Claude that describe its capabilities, limitations, known biases, and safety evaluations.
Explainability (XAI)
Explainability refers to the ability to understand and communicate how an AI system arrives at its decisions or predictions. An explainable model allows humans to inspect its reasoning, rather than treating it as an opaque 'black box'.
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.
AI Governance
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.
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.