The next administration should aim to make the United States a world leader in privacy-preserving machine learning (PPML), a collection of new artificial intelligence (AI) techniques capable of providing the benefits of machine learning while minimizing data-privacy concerns. By some estimates, improvements to the speed, accuracy, and scale of AI could augment global GDP by 14%, or $15.7 trillion, by 2030. Yet Americans fear that expansion of AI will have moderate to severe negative consequences. They are particularly concerned about the privacy implications of how companies and agencies use personal data to generate new developments. To assuage these concerns, this proposal recommends targeted initiatives for the next administration to bring PPML techniques to maturity including 1) investing in PPML research and development (R&D), 2) identifying compelling opportunities to apply PPML techniques at the federal level, and 3)creating frameworks and technical standards to facilitate wider deployment of PPML techniques.
About the Authors
Roxanne Heston is a Research Analyst at Georgetown’s Center for Security and Emerging Technology (CSET). She is concurrently a master's student in Georgetown's Security Studies Program. Roxanne previously assisted the research of former SECNAV Richard Danzig, Dr. Ben Buchanan, and Oxford University's Center for the Governance of AI. She received a B.S. in Economics with honors on a full scholarship from Tulane University, where she was an Altman Scholar in International Studies & Business.
Helen Toner is Director of Strategy at Georgetown’s Center for Security and Emerging Technology (CSET). She previously worked as a Senior Research Analyst at the Open Philanthropy Project, where she advised policymakers and grantmakers on AI policy and strategy. Between working at Open Philanthropy and joining CSET, Helen lived in Beijing for nine months, studying the Chinese AI ecosystem as a Research Affiliate of Oxford University’s Center for the Governance of AI.