Interpretability and Multiplicity: a Path to Trustworthy Machine Learning

Abstract

Machine learning has been increasingly deployed for myriad high-stakes decisions that deeply impact people's lives. This is concerning, because not every model can be trusted. Interpretability is crucial for making machine learning models trustworthy. It provides human-understandable reasons for each prediction. This, in turn, enables easier troubleshooting, responsible decision-making, and knowledge acquisition. However, there are two major challenges in using interpretable machine learning for high-stakes problems: (1) interpretable model optimization is often NP-hard, and (2) an inefficient feedback loop is present in the standard machine learning paradigm. My dissertation addresses these challenges and proposes a new paradigm for machine learning to advance trustworthy AI. I first tackle the challenge of finding interpretable-yet-accurate models. This involves developing efficient optimization algorithms. Models obtained from these algorithms are inherently interpretable while maintaining accuracy comparable to that of black-box counterparts. I then discuss the interaction bottleneck in the standard machine learning paradigm and propose a new paradigm, called learning Rashomon sets, which finds and stores all machine learning models with loss that is within epsilon of the optimal loss. This allows users unprecedented ability to explore and interact with all well-performing models, enabling them to choose and modify models that are best suited for the application.