Abstract: Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.
Bio: Irene Chen is a PhD student at MIT in Electrical Engineering and Computer Science in the Clinical Machine Learning Group. Her research focuses on building machine learning methods that to advance knowledge in health care and fairness. Before MIT, she received her AB/SM from Harvard in Applied Math and Computational Engineering and worked at Dropbox for two years as a Data Scientist, Machine Learning Engineer, and Chief of Staff.
This semester of the UMass Machine Learning and Friends Lunch (MLFL) series has been graciously sponsored by our friends at Oracle Labs.
MLFL is a lively and interactive forum held weekly where friends of the UMass Amherst machine learning community can sit down, have lunch, and give or hear a 50-minute presentation on recent machine learning research.
What is it? A gathering of students/faculty/staff with broad interest in the methods and applications of machine learning.
When is it? Thursdays 12:00pm to 1:00pm, unless otherwise noted. Arrive at 11:45 to get pizza.
Where is it? CS150
Who is invited? Everyone is welcome.
Is there food? Yes! Pizza is provided.
Can I present? Yes! If you would like to present your research, please email one of the organizers: Ari Kobren, Rajarshi Das, Hang Su, Samer Nashed and Aruni Roy Chowdhury