Vishwali Mhasawade

Bio: Vishwali Mhasawade is a Ph.D. candidate in Computer Science at New York University, advised by Prof. Rumi Chunara. Her research is supported by the Google Fellowship in Health. She focuses on designing fair and equitable machine learning systems for mitigating health disparities and developing methods in causal inference and algorithmic fairness. Vishwali was an intern at Fiddler AI Labs and Spotify Research. She has been involved in mentoring roles involving high school students through the NYU ARISE program, reviewer mentoring through the Machine Learning for Health initiative, and career mentoring for Ph.D. applicants through the Women in Machine Learning program.

Talk Title: Advancing Health Equity with Machine Learning

Talk Abstract: While a patient visits the hospital for treatment, factors outside the hospital, such as where the individual resides and what educational and vocational opportunities are present, play a vital role in the patient’s health trajectory. On the contrary, most advances in machine learning in healthcare are mainly restricted to data within hospitals and clinics. While health equity, defined as minimizing avoidable disparities in health and its determinants between groups of people with different social privileges in terms of power, wealth, and prestige, is the primary principle underlying public health research, this has been largely ignored by the current machine learning systems. Inequality at the social level is harmful to the population as a whole. Thus, a focus on the factors related to health outside the hospital is imperative to not only address specific challenges for high-risk individuals but also to determine what policies will benefit the community as a whole. In this talk, I will first demonstrate the challenges of mitigating health disparities resulting from the different representations of demographic groups based on attributes like gender and self-reported race. I will focus on machine learning systems using person-generated data and in-hospital data from multiple geographical locations worldwide. Next, I will present a causal remedial approach to health inequity using algorithmic fairness that reduces health disparities. In the end, I will discuss how algorithmic fairness can be leveraged to achieve health equity by incorporating social factors and illustrate how residual disparities persist if social factors are ignored.