This special track addresses current debates about the limits of pure model scaling and highlights why good AI is primarily a matter of design and governance. Following an introductory summary of the risks associated with variable quality and different approaches to evaluation, we explore a selection of practical case studies to demonstrate how the Data Fairness Label can be used as a framework for creating fair and robust AI systems.
A moderated panel discussion with experts from industry and practice will expand the conversation around experiences, challenges, and best practices.
Swiss Insights, Nicole Siegrist, Managing Director, Swiss Insights - Swiss Data Insights Association
Charlotte Malz, Moderator & Communications Specialist, Swiss Insights
