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Interpretable Machine Learning
Andrew Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands

Thu Dec 07 02:00 PM -- 09:30 PM (PST) @ Hall C
Event URL: http://interpretable.ml »

Complex machine learning models, such as deep neural networks, have recently achieved outstanding predictive performance in a wide range of applications, including visual object recognition, speech perception, language modeling, and information retrieval. There has since been an explosion of interest in interpreting the representations learned by these models, with profound implications for research into explainable ML, causality, safe AI, social science, automatic scientific discovery, human computer interaction (HCI), crowdsourcing, machine teaching, and AI ethics. This symposium is designed to broadly engage the machine learning community on these topics -- tying together many threads which are deeply related but often considered in isolation.

For example, we may build a complex model to predict crime activity. But by interpreting the learned structure of the model, we can gain new insights into the processes driving crime events, enabling us to develop more effective public policy. Moreover, if we learn that the model is making good predictions by discovering how the geometry of clusters of crime events affect future activity, we can use this knowledge to design even more successful predictive models. Similarly, if we wish to make AI systems deployed on self-driving cars safe, straightforward black-box models will not suffice, as we need methods of understanding their rare but costly mistakes.

The symposium will feature talks and panel discussions. One of the panels will have a moderated debate format where arguments are presented on each side of key topics chosen prior to the symposium, with the opportunity to follow-up each argument with questions. This format will encourage an interactive, lively, and rigorous discussion, working towards the shared goal of making intellectual progress on foundational questions. During the symposium, we will also feature the launch of a new Explainability in Machine Learning Challenge, involving the creation of new benchmarks for motivating the development of interpretable learning algorithms.

Author Information

Andrew Wilson (Cornell University)
Andrew Wilson

I am a professor of machine learning at New York University.

Jason Yosinski (Uber AI Labs; Recursion)

Dr. Jason Yosinski is a machine learning researcher, was a founding member of Uber AI Labs, and is scientific adviser to Recursion Pharmaceuticals and several other companies. His work focuses on building more capable and more understandable AI. As scientists and engineers build increasingly powerful AI systems, the abilities of these systems increase faster than does our understanding of them, motivating much of his work on AI Neuroscience: an emerging field of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski completed his PhD as a NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, XKCD, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches students algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, motorcycling, reading, paragliding, and sometimes pretending he's an artist.

Patrice Simard (Microsoft Research)
Rich Caruana (Microsoft)
William Herlands (Carnegie Mellon University)

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