Appendix
SACKLER FORUM ON MACHINE LEARNING PROGRAM
Machine learning is at the core of many applications that have become part of daily life, from voice recognition to image perception. These technologies, which a few years ago were performing at noticeably below-human levels, can now outperform people at some tasks. As the field continues to evolve, machine learning has the potential to play a transformative role across a diverse range of sectors including transportation, medicine, public services, and finance. This forum brought together scientists from the United Kingdom and the United States to explore potential applications for machine learning and discuss the legal and ethical questions that arise as humans and machine learning algorithms interact.
TUESDAY - JANUARY 31, 2017
| 9:00 AM | Welcome from the National Academy of Sciences and Royal Society |
| Diane Griffin, Vice President, National Academy of Sciences | |
| Richard Catlow, Foreign Secretary, Royal Society | |
| Welcome from the Co-Chairs | |
| Peter Donnelly, University of Oxford | |
| Michael Kearns, University of Pennsylvania |
Session 1: The Frontiers of Machine Learning
The ubiquity of data, accessibility of computing power, and algorithmic advances have driven rapid progress in machine learning over the past five years. Not only does machine learning now underpin many applications that have become part of daily life, the field continues to evolve quickly and has the potential to play a transformative role across a diverse range of sectors. This session explored the frontiers of machine learning, in terms of both cutting-edge technology and near-term applications, and discussed the state of the art of machine learning.
| 9:15 AM | I Know it’s an Idiot but it’s MY Artificial Idiot! |
| Vint Cerf, Google | |
| 9:50 AM | Towards Affordable Self-Driving Cars |
| Raquel Urtasun, University of Toronto | |
| 10:25 AM | Probabilistic Machine Learning: Foundations and Frontiers |
| Zoubin Ghahramani, University of Cambridge |
| 11:00 AM | Break |
| 11:30 AM | Words, Pictures, and Common Sense |
| Devi Parikh, Georgia Institute of Technology | |
| 12:05 PM | Applied Machine Learning at Google |
| Greg Corrado, Google | |
| 12:40 PM | Lunch |
| 1:40 PM | Panel Discussion |
| Vint Cerf, Google | |
| Raquel Urtasun, University of Toronto | |
| Zoubin Ghahramani, University of Cambridge | |
| Devi Parikh, Georgia Institute of Technology | |
| Greg Corrado, Google |
Session 2: Machine Learning and Society
People and machine learning increasingly interact in a range of contexts. This expansion of machine learning raises legal and ethical questions, re-frames discussions about uses of data, and poses new challenges for the governance of this technology. The social acceptability of different machine learning applications, desirability of automated decision-making processes, adequacy of processes to manage concerns about statistical stereotyping or privacy, and more, will all influence how and where society has confidence in the deployment of machine learning systems. This session explored the societal implications of machine learning and the opportunities and challenges associated with advances in the field.
| 2:25 PM | Artificial Intelligence and Life in 2030 |
| Peter Stone, University of Texas at Austin | |
| 3:00 PM | Interpretable Machine Learning for Recidivism Prediction |
| Cynthia Rudin, Duke University | |
| 3:35 PM | Break |
| 4:10 PM | Protecting and Enhancing Our Humanity in an Age of Machine Learning |
| Charis Thompson, University of California, Berkeley |
| 4:45 PM | Using Machine Learning in Criminal Justice Risk Assessments Richard Berk, University of Pennsylvania |
| 5:20 PM | Adjourn for the day |
WEDNESDAY - FEBRUARY 1, 2017
| 9:00 AM | Welcome from the Co-Chairs |
| Peter Donnelly, University of Oxford | |
| Michael Kearns, University of Pennsylvania | |
| 9:05 AM | Privacy and Machine Learning: Promise, Peril, and the Path Forward |
| Pam Dixon, World Privacy Forum | |
| 9:40 AM | Algorithmic Regulation: A Critical Interrogation |
| Karen Yeung, King’s College London | |
| 10:15 AM | Break |
| 10:45 AM | Panel Discussion |
| Peter Stone, University of Texas at Austin | |
| Cynthia Rudin, Duke University | |
| Charis Thompson, University of California, Berkeley | |
| Richard Berk, University of Pennsylvania | |
| Pam Dixon, World Privacy Forum | |
| Karen Yeung, King’s College London | |
| 11:45 AM | Lunch |
Session 3: Machine Learning in Research and Commercial Communities
There are enormous opportunities in machine learning in academia, research labs, and industry. While much of the research and development of machine learning to date has been done in the commercial world, each of these communities will continue advancing this field. Establishing key research challenges and areas of commercial opportunity will therefore be important in moving the frontiers of machine learning forward. This session explored key areas of interest in machine learning in the research and commercial communities.
| 1:00 PM | Building the Human Wiring Diagram from Linked Genomic and Healthcare Data |
| Gil McVean, University of Oxford | |
| 1:35 PM | Active Optimization and Self-Driving Cars |
| Jeff Schneider, Carnegie Mellon University and Uber Advanced Technology Center |
| 2:10 PM | Three Principles for Data Science: Predictability, Stability, and Computability Bin Yu, University of California, Berkeley |
| 2:45 PM | Experimental Design and Machine Learning Opportunities in Mobile Health |
| Susan Murphy, University of Michigan | |
| 3:20 PM | Break |
| 3:40 PM | A Deployable Decision Service |
| John Langford, Microsoft Research | |
| 4:15 PM | Panel Discussion |
| Jeff Schneider, Carnegie Mellon University | |
| Bin Yu, University of California, Berkeley | |
| Susan Murphy, University of Michigan | |
| Gil McVean, University of Oxford | |
| John Langford, Microsoft Research | |
| 4:55 PM | Adjourn Meeting |
