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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.
As AI systems are given more autonomy — from driving cars to managing energy grids — ensuring they operate safely and predictably becomes a matter of public safety.
Researchers at DeepMind work on AI safety problems such as preventing reinforcement learning agents from finding dangerous shortcuts to achieve their goals.
Alignment
Alignment refers to the challenge of ensuring that an AI system's goals and behaviours match human values and intentions. A misaligned system might technically achieve its objective while producing harmful or undesirable side effects.
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.