Statistical Aspects of Trustworthy Machine Learning (24w5284)

Organizers

(University of Connecticut)

Xiaotong Shen (University of Minnesota)

Hao Zhang (University of Arizona)

Stephanie Hicks (Johns Hopkins)

(University of British Columbia)

Description

The Banff International Research Station will host the "Statistical Aspects of Trustworthy Machine Learning" workshop in Banff from February 11 - 16, 2024.


Machine learning algorithms are increasingly being deployed in a wide range of domains, including medicine, advertising, criminal justice, speech recognition, and computer vision. These applications have the potential for significant impacts on our daily lives, but widespread acceptance by society has lagged behind due to the lack of trust. While the field of statistics has played an integral role in the machine learning revolution, much of its attention has focused on developing accurate algorithms. In addition to being accurate, however, trustworthy machine learning methods must also be fair, transparent, and interpretable. This workshop will bring together members of the statistical community to discuss recent progress and potential statistical solutions toward these understudied aspects of trustworthy machine learning.


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).