Trustworthy Machine Learning

Catalog description

Recent advances in machine learning---in particular deep neural networks and large language models, are transforming the design and implementation of decision-making systems. However, due to their black-box nature, brittleness, and lack of safety guarantees, significant challenges remain in their adoption in critical and potentially high payoff applications such as autonomous systems and healthcare. The vibrant field of "Trustworthy ML" is developing methods and tools to address questions such as: how can we ensure that a decision recommended by an ML system is always safe? how can we explain the decision made by an ML system to a stakeholder? how can we ensure that an ML system makes its decisions in a fair and ethical manner? The goal of this course is to introduce students to state-of-the-art research in trustworthy ML. Note that topics of bias, privacy, and ethics, while also central to the field of trustworthy ML, are covered in CIS 4230 “Ethical Algorithm Design”.