RISE Seminar 4/5/19: AI Ethics for Systems (and other technical) Researchers, a talk by Sarah Bird
April 5, 2019
Title: AI Ethics for Systems (and other technical) Researchers
Speaker: Sarah Bird
Date and location: Friday, April 5, 12:30 – 1:30pm; Wozniak Lounge (430 Soda Hall)
Abstract: Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and “to engineer responsibility into the very fabric of the technology.” This of course has significant philosophical and political challenges and tradeoffs, and much of the conversation has been centered around these issues. However, while we are working on these social issues, there is still a lot of progress that can be made on the technical front towards making it easier to develop AI responsibly. In this talk, I will share some of what I have learned in practice as a result of trying to understand and improve the fairness of several major AI products in industry. I will share my prospective on really matters for technical people to understand about the issues and present some key research and technology problems that I think we can innovate on now in systems and machine learning to enable responsible AI development.
Bio: Sarah leads research and emerging technology strategy for AI developer products in Azure. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations in machine learning and systems research with the best of open source and product expertise to create new tools and technologies. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, an open source standard for machine learning models and was a leader in the PyTorch 1.0 project.
Sarah’s research interests include machine learning systems and responsible AI. She was an early member of the machine learning systems research community and has been active in growing and forming the community. Previously, Sarah was a machine learning systems researcher in Microsoft Research NYC, where she worked on reinforcement learning systems and AI ethics. She co-founded the SysML research conference and the Learning Systems workshops. She has a Ph.D. in computer science from UC Berkeley advised by Dave Patterson, Krste Asanovic, and Burton Smith.