Dr. Daniel S. Schiff is an Assistant Professor of Technology Policy in the Department of Political Science at Purdue University and founding Co-Director of GRAIL, the Governance and Responsible AI Lab. As a policy scientist with a background in philosophy, he studies the formal and informal governance of AI through policy and industry, as well as AI’s social and ethical implications in domains like education, labor, misinformation, and criminal justice.
His interdisciplinary research examines agenda-setting, standards, and regulatory processes; the organizational practices and principles-to-practice gaps that shape responsible AI in industry; deepfakes and synthetic media; public attitudes, participation, and influence in AI politics; and AI education, ethics, and literacy. His work has been published in venues including the American Political Science Review, Management Science, Policy Studies Journal, PNAS Nexus, Science and Public Policy, Public Administration Review, Technology in Society, AI & Society, the International Journal of AI in Education, IEEE Transactions on Technology and Society, and the AAAI/ACM Conference on AI, Ethics, and Society.
Before academia, Daniel served as the founding Responsible AI Lead at JP Morgan Chase & Co., Secretary of the IEEE 7010-2020 standard - the first AI ethics industry standard focused on the impacts of AI on human well-being - and Director of Research, Evaluation, and Planning at the Philadelphia Education Fund. His research and commentary have been featured in outlets such as the New York Times, Washington Post, CNN, WIRED, The Atlantic, and MIT Technology Review, and his work has informed policy efforts by the OECD, the European Parliament, Australia’s National Science Agency, and the U.S. Department of Commerce.
He studied Philosophy at Princeton University, with an emphasis on robotics and intelligent systems, where his undergraduate thesis was titled The Ethics of Artificial Intelligence - an early signal of a focus on AI ethics and governance he has pursued since the late 2000s, before the deep learning revolution, as part of the early scholarship helping define those fields. He later completed an MS in Social Policy at the University of Pennsylvania and a PhD in Public Policy at the Georgia Institute of Technology.