Machine learning has enabled tremendously exciting technologies, but at the same time it raises questions as to how it should be deployed in a responsible and trustworthy manner. How can machine learning be made secure, reliable, robust, fair, and private? This workshop will explore the information-theoretic foundations of these aspects of machine learning. The workshop will include invited talks by experts on these topics from both academy and industry, student poster presentations, and time for fruitful discussions. Keynote talks will be given by Tara Javidi, Ilya Mironov, Todd Coleman, and Ayfer Ozgur.

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a set of computational techniques that allow one to accurately compute marginal and joint entropies. These algorithms are probabilistic in nature and thus provide information on the uncertainty in our estimates, which enable us to establish statistical significance of our findings. We demonstrate these methods by identifying relations between cloud data from the International Satellite Cloud Climatology Project (ISCCP) and data from other sources, such as equatorial pacific sea surface temperatures (SST).