Jon Penney, "Privacy Lessons for Risk-Based AI Regulation" скачать в хорошем качестве

Jon Penney, "Privacy Lessons for Risk-Based AI Regulation"

This was the February talk in a quarterly speaker series co-presented by the Johns Hopkins Institute for Assured Autonomy (IAA) and Berman Institute of Bioethics, featuring national scholars presenting new research and development at the intersection of autonomy and assurance. This talk was “Privacy Lessons for Risk-Based AI Regulation,” featuring speaker Jon Penney, Associate Professor at Osgoode Hall Law School, York University; Faculty Associate at Harvard’s Berkman Klein Center for Internet & Society; and Research Fellow at The Citizen Lab based at the University of Toronto’s Munk School of Global Affairs and Public Policy, presented virtually on February 21, 2025. ABSTRACT: A combination of ubiquitous computing, big data, and the development and deployment of artificial intelligence (AI) and machine learning (ML) systems across all sectors of society has created immense new possibilities, but also serious new risks and harms for privacy, safety, and human rights. Today, the consensus approach to AI regulation internationally is risk-based approaches. Lawmakers in the United States, Europe, Canada, and beyond have all turned to risk-based regulatory tools and schemes to regulate and govern AI systems. But because data is essential to the use and development of AI systems, AI data governance is likewise seen as essential to comprehensive AI regulatory schemes. The result is that data protection and governance is often tacked onto, or bootstrapped to, these broader risk-based approaches, with the EU’s Artificial Intelligence Act—often described as the most robust and comprehensive AI regulatory scheme internationally—a good example of this. While there is a lively debate about the wisdom of risk-based approaches in AI scholarship and public policy, much less has been said about the wisdom of risk-based approaches for AI data privacy and governance. That is the focus of this talk. Drawing on lessons from privacy and data protection law, policy, and research, this talk argues that the risk-based approaches to AI regulation predominant today are not only largely incommensurable with robust protection for data privacy interests, but need to be fundamentally re-oriented—or entirely abandoned—to address the real risks and harms of AI systems today and tomorrow. About the Johns Hopkins Institute for Assured Autonomy: Led by APL and the Whiting School of Engineering, the IAA is becoming a nationally recognized center of excellence in autonomous systems, showcasing the robust portfolio of research and work from two premier divisions of JHU and creating strategic external partnerships. The IAA seeks to ensure the safe, secure, and reliable integration of autonomous systems and artificial intelligence (AI) in society. As autonomous systems proliferate, both physically and virtually, the institute seeks to ensure the systems will be trusted and safe in their operations, will withstand corruption by adversaries, and will integrate seamlessly into ecosystems and communities. In this burgeoning field, JHU strives to advance a clear vision for an autonomous future.