Managing Algorithmic Bias and Fairness in AI for Health Care (On-Demand Webinar)

Date: 07/09/24
Closed captions are available.

Overview: This webinar addresses how to navigate bias in AI applications as well as various approaches to identify, mitigate, and monitor the impact of bias using various solutions throughout the AI lifecycle.

Learning Objectives: 

• Attorneys must anticipate the risks associated with this technology so they can effectively advise their clients on how to best manage them.
• Learn about how when algorithms are infused with biased information or without recognition of existing bias, they may further entrench health care inequities.
  
• Overview of the law affecting health equity and government oversight of AI discrimination.
  

Speaker Information:

Brad M. Thompson, Shareholder, Epstein Becker & Green PC

Chris Provan, Managing Director & Senior Principal Data Scientist, Mosaic Data Science

Sam Tyner-Monroe Ph.D, Managing Director, Responsible AI, DLA Piper

Credit Information:

CLE: The maximum number of credits available for ON DEMAND is 1.5 for a 60-minute state and 1.8 for a 50-minute state. Please note that the availability of credits may vary from state to state.  This self-study course will be available for purchase for approximately one year after the recording date, but state rules on duration of eligibility for CLE-credits differ, so please check with your state before purchasing self-study offerings. 

CPE: CPE credits are not available for on-demand.

CCB: CCB credits are not available for this on-demand webinar.