Academic Keynote Speaker: Ariel Procaccia
Bio: Ariel Procaccia is Gordon McKay Professor of Computer Science at Harvard University. He works on a broad and dynamic set of problems related to AI, algorithms, economics, and society. His distinctions include the Social Choice and Welfare Prize (2020), a Guggenheim Fellowship (2018), the IJCAI Computers and Thought Award (2015), and a Sloan Research Fellowship (2015). To make his research accessible to the public, he has founded the not-for-profit website Spliddit.org and he regularly contributes opinion pieces.
Talk Title: Democracy and the Pursuit of Randomness
Abstract: Sortition is a storied paradigm of democracy built on the idea of choosing representatives through lotteries instead of elections. In recent years this idea has found renewed popularity in the form of citizens’ assemblies, which bring together randomly selected people from all walks of life to discuss key questions and deliver policy recommendations. A principled approach to sortition, however, must resolve the tension between two competing requirements: that the demographic composition of citizens’ assemblies reflect the general population and that every person be given a fair chance (literally) to participate. I will describe our work on designing, analyzing and implementing randomized participant selection algorithms that balance these two requirements. I will also discuss practical challenges in sortition based on experience with the adoption and deployment of our open-source system, Panelot.
Industry Keynote Speaker: Rich Caruana
Bio: Rich Caruana is a senior principal researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty in the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery. Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped create interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, transparent modeling, deep learning, and computational ecology.
Talk Title: Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning
Abstract: In machine learning often tradeoffs must be made between accuracy, privacy and intelligibility: the most accurate models usually are not all that intelligible or private, and the most intelligible or private models usually are less accurate. This can limit the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust models is important. EBMs (Explainable Boosting Machines) are a recent learning method based on generalized additive models (GAMs) that are as accurate as full complexity models, more intelligible than linear models, and which can be made differentially private with little loss in accuracy. EBMs make it easy to understand what a model has learned and to edit the model when it learns inappropriate things. In the talk I’ll present case studies where EBMs discover surprising patterns in data that would have made deploying black-box models risky. I’ll also show how we’re using these models to uncover and mitigate bias in models where fairness and transparency are important.