Addressing regulatory challenges for AI in healthcare: Building a safe and effective machine learning life cycle
Speaker: Adarsh Subbaswamy, Johns Hopkins University
Location: 60 Fifth Avenue 150
Date: February 28, 2022, 2 p.m.
Host: Rajesh Ranganath
As machine learning (ML) is beginning to power technologies in high impact domains such as healthcare, the need for safe and reliable machine learning has been recognized at a national level. For example, the U.S. Food and Drug Administration (FDA) has recently had to rethink its regulatory framework for the ever growing number of machine learning-powered medical devices. The core challenge for these agencies is to determine whether machine learning models will be safe and effective for their intended use. In my research, I seek to develop safe and effective machine learning that can meet the needs of various stakeholders—including users, model developers, and regulators. Accomplishing this requires addressing technical challenges at all stages of a machine learning system’s life cycle, from new learning algorithms that allow users to specify desirable behavior, to stress-tests and verification of safety properties, to model monitoring and maintenance strategies. In this talk, I will overview my work addressing various parts of the machine learning life cycle with respect to the problem of dataset shift—differences between the model's training and deployment environments that can lead to failure to generalize. First, I will describe causally-inspired learning algorithms which allow model developers to specify potentially problematic dataset shifts ahead of time and then learn models which are guaranteed to be stable to these shifts. Then I will describe a new evaluation method for stress-testing a model's stability to dataset shift. This is generally a difficult task because it requires evaluating the model on a large number of independent datasets. Since the cost of collecting such datasets is often prohibitive, I will describe a distributionally robust framework for evaluating model robustness to user-specified shifts using only the available evaluation data.
Adarsh Subbaswamy is a PhD candidate in computer science at Johns Hopkins University advised by Suchi Saria, and a CERSI scholar affiliated with the Johns Hopkins Center of Excellence in Regulatory Science and Innovation. His research seeks to address challenges in developing safe and effective machine learning for safety-critical domains such as healthcare using techniques from machine learning, causal inference, and robust optimization. His work has appeared in machine learning conferences (e.g., AISTATS and UAI) as well as medical journals (Biostatistics and the New England Journal of Medicine). Prior to his PhD, Adarsh received his BS from Vanderbilt University.
In-person attendance only available to those with active NYU ID cards.