Prerequisite: None
This session will include a moderated Q&A featuring questions from the live audience.
The emergence of large language models, machine learning, and other AI technologies has revolutionized the way data, analytics, and decision-making are used in business. AI processes large data volumes in order to identify patterns, answer questions, and generate insights. The capabilities are striking—but there are limitations. It’s susceptible to errors: bias, data misuse, and possibly law or policy violation. AI governance has emerged as a discipline focusing on the ethics, integrity, and auditability of data usage and processing.
In this talk, expert practitioner Evan Levy will review real-world examples of AI governance and data governance, then discuss their similarities, differences, and intertwined relationship. Levy will also discuss the common structures, the stakeholders, and why both are crucial for analytics and AI development success. Key concepts will include:
- The needs and drivers for establishing an AI governance program, with some real-world examples
- The differences and similarities between data governance and AI governance
- The basic governance structures and mechanisms necessary for any type of governance program
- The benefits and trade-offs of establishing separate AI governance and data governance programs