AI governance frameworks could help organizations learn, govern, monitor, and mature AI adoption and scale. While there is no one-size-fits-all approach, organizations can consider adopting processes to mitigate risk. This session will explore:
- What an effective AI governance and risk management framework looks like in practice
- The core principles that can be operationalized
- Implementation of a functional framework irrespective of available resources and organization size
- The most vital aspects of a framework and how to tailor them based on need
- Generating maximum additional value as a result
Gurleen Virk
Ken Archer
Daniel Wu
Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.
Daniel is passionate about the democratization of technology and the ethical use of AI - a philosophy he shares in the computer science and AI/ML education programs he has contributed to over the years.