The Mainstays of Trustworthy Machine Learning

Abstract

While machine learning (ML) models of today have the potential to be useful in many societal applications, they also harbor the potential for great harm, be it perpetuating biases or compromising privacy. To prevent these harms, many (evolving) regulatory guardrails have been put in place; for instance European Union's GDPR and Biden's Executive Order which demand explainability, privacy, fairness and so on from models deployed in societal applications. Yet, most technical solutions in the Trustworthy ML literature which claim to meet these regulatory requirements are brittle and often fail at the task in hand. To this end, my research aims to make the field of Trustworthy ML reliable using mainstay concepts of Measurement, Mitigation and Maintenance. With these concepts, I develop end-to-end solutions for trustworthy ML by (1) exploring the limitations of existing approaches and (2) providing principled novel solutions exploiting interconnections with cryptography.