Provably Fair and Robust Machine Learning

Provably Fair and Robust Machine Learning

Explore cutting-edge research on building provably fair and robust machine learning systems in this conference talk delivered by Prof. Martin Vechev from ETH at the INSAIT 2022 Conference. Delve into fundamental challenges faced when deploying machine learning in real-world applications, with particular focus on both tabular and vision data. Learn about deterministic and statistical certification methods that ensure fairness and robustness in ML systems. The 37-minute presentation addresses critical aspects of machine learning deployment, offering insights into creating more reliable and equitable AI systems.