Title: Fairness in Machine Learning
Speaker: Tulsee Doshi
Date: February 26, 2020
ML Fairness is a critical consideration in machine learning development. As we build machine learning models intended for a global and diverse user base, it is also important to ensure that the outcome is inclusive of that user base. To truly address fairness requires designing and developing products with diverse stakeholders in the room and a deep understanding of the impacts and factors at play. There are, however, approaches that can help in the evaluation and mitigation of common fairness concerns. In this talk, I will present a few lessons Google has learned through our products and research, and share some of the approaches developers can take to evaluate and improve fairness concerns. Lastly, I will touch on the importance of explainability in addressing fairness concerns, and the tools and techniques that are available in this space.
Product Lead for Google's Machine Learning Fairness Effort
Tulsee Doshi is the product lead for Google’s ML fairness effort, where she leads the development of Google-wide resources and best practices for developing more inclusive and diverse products. Previously, Tulsee worked on the YouTube recommendations team. She studied Symbolic Systems and Computer Science at Stanford University.
Director of Accessibility, Google; Member, ACM Practitioners Board
Eve Andersson is Director of Accessibility at Google, where she also previously worked on Inclusive AI. Prior to joining Google, Andersson was Senior Vice President of Academics at Neumont University. She also co-founded ArsDigita Corporation, an open-source software company that was acquired by Red Hat, and she was Visiting Professor of Computer Science at Universidad Galileo in Guatemala City. Eve has co-authored two books: Software Engineering for Internet Applications (MIT Press, 2006) and Early Adopter VoiceXML (Wrox Press, 2001). Eve holds Engineering degrees from Caltech and U.C. Berkeley and an MBA in Finance from Wharton. She is based in San Francisco; prior to this, she lived in Argentina, Guatemala, the UK, and various US cities. Her interests include travel, photography, the number pi, and working on her own software side projects. As an ACM volunteer, Eve is Chair of Professional Development Committee and a member of the Practitioners Board.