Today's Speaker
William La Cava (Harvard Medical School, Boston Children's Hospital), "Unfairness in AI-based Clinical Decisions: Intersectional Approaches to Measurement and Mitigation"
Clinical decision support systems increasingly rely on machine learning (ML) models to recommend courses of action. As a result, these systems have the potential to exacerbate inequities in healthcare allocation and disadvantage historically and contemporarily marginalized groups. To address this risk, fair ML algorithms have been proposed that minimize differences in model performance among patient groups. I will discuss some of these methods and the challenges to implementing them in practice. Two major challenges are to measure and mitigate these differences when we consider grouping patients by intersections of demographic variables such as age, race, ethnicity, sex, and socio-economic status.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
LOCATION:CGIS Knafel, room K354 STATUS:CONFIRMED DTSTART:20220202T170000Z DTEND:20220202T183000Z END:VEVENT END:VCALENDAR