photo of Dewey Murdick

Dewey Murdick

Director of Data Science - Center for Security and Emerging Technology (CSET) School of Foreign Service, Georgetown University

Dewey Murdick is the Director of Data Science at Georgetown University’s Center for Security and Emerging Technology. Dewey leads a team of data scientists & engineers and language & survey specialists to help leaders make scientific, technical, and related mission-critical decisions. Dewey’s research interests include connecting research and emerging technology to future capabilities, emerging technology forecasting, strategic planning, and portfolio management in support of data-informed policy analysis. Prior to joining Georgetown, he was the Director of Science Analytics at the Chan Zuckerberg Initiative, where he led metric development, data science, and machine-learning and statistical research for Meta and science-related initiatives. Dewey led research and development portfolio analysis and advised on forecasting system development as Chief Analytics Officer and Deputy Chief Scientist within the U.S. Department of Homeland Security (DHS). Dewey holds a PhD in Engineering Physics from the University of Virginia and a BS in Physics from Andrews University.

Dewey Murdick's videos

The OECD Framework for the Classification of AI Systems

The OECD Framework for the Classification of AI Systems

February 2, 2021clock4 mins

Different types of AI systems raise very different policy opportunities and challenges. As part of the AI-WIPS project, the OECD has developed a user-friendly framework to classify AI systems. The framework provides a structure for assessing and classifying AI systems according to their impact on public policy in areas covered by the OECD AI Principles.

The OECD Al Systems Classification Framework

The OECD Al Systems Classification Framework

February 6, 2021clock90 mins

The OECD’s Network of Experts on AI developed a user-friendly framework to classify AI systems. It provides a structure for assessing and classifying AI systems according to their impact on public policy following the OECD AI Principles. This session discusses the four dimensions of the draft OECD AI Systems Classification Framework, illustrates the usefulness of the framework using concrete AI systems as examples, and seeks feedback and comments to support finalisation of the framework. Aclassification framework to understand the labour market impact will also be introduced.

AI System Classification for Policymakers

AI System Classification for Policymakers

January 28, 2021clock13 mins

Disclaimer :The opinions expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the official views of the OECD or its member countries. The Organisation cannot be held responsible for possible violations of copyright resulting from the posting of any written material on this website/blog.

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