BIDS Machine Learning and Science Forum — ML for Social Good in Practice: from Robust Optimization to Broader Impacts in Education

ML&Sci Forum

October 25, 2021
11:00am to 12:00pm
Virtual Participation

BIDS Machine Learning and Science Forum
Date: Monday, October 25, 2021
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link 

ML for Social Good in Practice: from Robust Optimization to Broader Impacts in Education

Speaker: Serena Wang, PhD student, Computer Science, UC Berkeley
Abstract: The widespread use of machine learning techniques in social settings remains controversial, with a variety of recent examples in education, healthcare, and criminal justice. In response, the machine learning community has produced a wide range of fairness measures that theoretically address different forms of algorithmic bias, but applying these measures in practice under noisy data or modern privacy requirements is no longer so theoretically clean. The first part of this talk will cover new methods applying robust optimization to handle fairness constraints under noisy protected group information. But fairness constraints are only part of the story - the second part of this talk will expand the picture of positive societal impact to a broader question of how ML can better support real world societal principles and goals. Using the education domain as a case study, we examine whether the stated or implied societal objectives of papers from highly-regarded ML conferences are aligned with the ML problem formulation, objectives, and interpretation of results. Through the lens of interviews with education domain experts, we expand the view of the ML life cycle to include a deeper dive into problem formulation and the translation from predictions to interventions.

The BIDS Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by BIDS Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and Ben Nachman (physicist at Lawrence Berkeley National Laboratory), these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research.  To receive email notifications about upcoming meetings, or to request more information, please contact the organizers at berkeleymlforum@gmail.comAll interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. 


Serena Wang

PhD Student, Computer Science, UC Berkeley