Responsible AI decisions are not black and white, they require trade-offs. Learn to make trade-offs and debate the alternatives to make AI governance and responsible AI decisions. Discuss controversial ideas in AI with your peers. Express your opinion and listen to what others are saying. Learn to ask the right questions and get the right answers to ensure responsible, trustworthy and ethical AI.

See through the hype to better understand the practical applications, risks, and aspirations of AI in the data governance industry. Find out how to position your data governance program as a leader for your organizations' AI initiatives.

Join this session to get oriented about where you are on the AI maturity curve and develop the next steps to achieve your goals. Explore AI maturity by addressing its building blocks such as use case selection, technology operationalization and AI adoption.

KIL Proposal:
Implement your target AI governance operating model by mapping governance pillars to key AI components and differentiating AI capabilities. This model should connect with other governance bodies and extend existing governance models to AI-specific considerations of trust, transparency and diversity.