Events

Achieving machine learning's significant potential to promote innovative, trustworthy outcomes will require that relevant legal levers, including intellectual property, liability, privacy, and agency-specific regulation, be calibrated appropriately. This session will provide an overview of key legal regimes and proposals for calibration. For example, in high-risk areas like health, how much disclosure regarding how a model was trained do regulatory agencies like the FDA or adopters in the marketplace need in order to trust the model's outputs? How do we balance the benefits of transparent data with risks to privacy and trade secrecy needs? Who should bear financial responsibility if and when a model makes mistakes?

This session is part of the Duke + Data Science (+DS) program in-person learning experiences (IPLEs). To learn more, please visit https://plus.datascience.duke.edu/