CIS Seminar: “Building the Reliability Stack for Machine Learning”

Currently, machine learning (ML) systems have impressive performance but can behave in unexpected ways. These systems latch onto unintuitive patterns and are easily compromised, a source of grave concern for deployed ML in settings such as healthcare, security, and autonomous driving. In this talk, I will discuss how we can redesign the core ML pipeline to create reliable systems. First, I will show how to train provably robust models, which enables formal robustness guarantees for complex deep networks. Next, I will demonstrate how to make ML models more debuggable. This amplifies our ability to diagnose failure modes, such as hidden biases or spurious correlations. To conclude, I will discuss how we can build upon this “reliability stack” to enable broader robustness requirements, and develop new primitives that make ML debuggable by design.