Interpretability in Artificial Intelligence (22w5055)


(IBM - Zurich Research Lab)

Joaquin Dopazo (Fundación Progreso y Salud)

Carlos Loucera (Fundación Progreso y Salud)

An-phi Nguyen (ETH)


The Banff International Research Station will host the "Interpretability in Artificial Intelligence" workshop in Banff from May 1 - 6, 2022.

State-of-the-art deep learning networks are achieving strong predictive power, but the gain in accuracy often comes at the price of transparency, meaning that the prediction of the model is not interpretable. These so-called black-box models raise critical challenges in high-stake domains, such as healthcare, crime recidivism, or finance, where a wrong decision can have very harmful consequences. For instance, a tool to risk-stratify patients trained on a very unbalanced datasets can assign all new cases to the most prevalent risk-category, failing hence to identify clinical factors that might separate high- and low-risk patients. In other cases, ethnic, economic, or social factors, which might by chance correlate with a patient group, might be wrongly used by the model to classify new patients.

This workshop explores new developments in the field of interpretable AI that aim to design models where the reasons for a particular prediction are transparent, and the variables that contributed the most to such predictions can be identified. Such approaches can increase the trust in a model, and hence, accelerate the adoption of artificial intelligence approaches in sensitive domains.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).