arXiv · 2606.04490
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Abstract
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.
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Alexander K. Saeri, Jess Graham, Michael Noetel, Peter Slattery, Dennis Ah-king, Edla Aittokallio, Ibitola Akindehin, Abbas Al Mahdi, Elie Alhajjar, Rafael Andersson Lipcsey, Gary Ang, Catherine M. Azam, Amos Azaria, Rishal Balkissoon, Isabel Barberá, Claudio Bareato, Jonathan Barry, Michael Basehart, Andrew M. Bean, Danny Belitz, Samantha Augusta Bennett, Kayla Blomquist, Damian Borstel, Ben Bucknall, Tomas Bueno Momcilovic, Aurelie Bugeau, Nicholas Caputo, Stephen Casper, Gulam Chagani, Ze Shen Chin, Jiyeon Cho, Jay Chooi, Joel N. Christoph, Dmytro Chumachenko, Kieran Conboy, Elizabeth M. Daly, Tom David, Paul de Font-Reaulx, Antonio De Santis, Fabrizio Degni, Christopher W. DiCarlo, Yawen Duan, Janet Egan, Ian W. Eisenberg, Sherif M. Elsafty, Adam Ennamli, Mark Esposito, Nicola Fabiano, Gallo Fall, Neil R. Fernandes, Pip Foweraker, Chiara Gallese, Sandra Galletti, Andrew Gamino-Cheong, Rokas Gipiškis, Gwyn Glasser, Delaram Golpayegani, Jeff Grayson, Hans Gundlach, Josiah Hagen, Alexander Hagenah, Amelia S. Haines, The Anh Han, Yixiong Hao, Kasii Harris, Tianxing He, Koen Holtman, Giorgos Iacovides, Kenneth L. Ingham, Krystal Jackson, Adam Jones, Himanshu Joshi, Brian Judge, Arturs Kanepajs, Shreya Kapoor, Win Myat Nwe Khine, Aidan Kierans, Aleksandra Korolova, Markus Krebsz, Nicholas Kruus, Joe Kwon, Valeria Lazzaroli, Ray X. Lee, Evelina Leivada, Stephan Lewandowsky, Michael B. Li, Xiaojian Li, Geunsik Lim, Henrique Lisakowski, Fabio Lonardoni, Todd C. Lowe, Jackson G. Lu, Alexander Lyzhov, Nada Madkour, Parv Mahajan, David Manheim, Kareem Mathias, Claudio Mayrink Verdun, Sean McGregor, Scott McLean. 2026-06-03. Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts. https://arxiv.org/abs/2606.04490
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