Statistical Aspects of Trustworthy Machine Learning and Artificial Intelligence

Oregon Convention Center 

The statistical aspects of trustworthy machine learning (ML) and artificial intelligence (AI) have not been extensively studied, though Statistics has long played a critical role in the arena. When there is lack of consideration of interpretability, uncertainty quantification, limited/incomplete data, and selection bias, statistical methods may offer reasonable solutions. Popular and influential as ML/AI has become, some of the breakthroughs in statistics in the last 50 years have fueled this revolution, such as bootstrap, causal inference, deep learning, and exploratory data analysis, among others. Much effort from the statistical community is needed to tackle the open problems of trustworthy machine learning. We will review the exchanges of researchers at a Banff International Research Station workshop on statistical aspects of trustworthy ML, which was held in February 2024. Specific topics include interpretability, privacy-preservation, robustness, and fairness.