AI Governance and Assurance for Standards, Conformity Assessment and Regulatory Authorities.
Artificial intelligence is rapidly transforming how organisations develop standards, assess conformity, conduct inspections, analyse regulatory information and make compliance decisions. For standards and regulatory authorities, AI presents significant opportunities to improve efficiency, regulatory intelligence, market surveillance, risk assessment and decision-making, but it also introduces new risks relating to accuracy, transparency, bias, accountability, data protection and cybersecurity.
The AI Governance and Assurance for Standards, Conformity Assessment and Regulatory Authorities programme is designed to equip professionals with the knowledge and practical capabilities required to govern, assess, monitor and responsibly deploy AI systems within standards, regulatory and conformity-assessment environments.
The programme moves beyond general AI awareness and focuses specifically on the governance, assurance and regulatory dimensions of AI. Participants will examine how AI systems should be evaluated before and after deployment, how AI-related risks can be identified and controlled, and how institutions can establish appropriate governance structures, policies, accountability mechanisms and assurance processes.
Key areas will include AI governance frameworks, responsible AI, algorithmic accountability, AI risk management, model validation, bias and fairness, explainability, transparency, data governance, cybersecurity, human oversight and AI assurance. Participants will also explore the implications of AI for inspection, testing, certification, product compliance, market surveillance and regulatory decision-making.
Particular attention will be given to the emerging challenge of AI-assisted regulatory decisions. Participants will learn how regulators can use AI while ensuring that decisions remain transparent, evidence-based, auditable and subject to appropriate human oversight. The programme will also examine how AI-generated information can be verified and how institutions can manage risks associated with inaccurate or unreliable AI outputs.
Through international case studies, regulatory scenarios, practical risk-assessment exercises and governance workshops, participants will develop the ability to assess the readiness, risks and assurance requirements of AI applications within their institutions.