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arxiv: 2606.04490 · v1 · pith:SKD7HKJHnew · submitted 2026-06-03 · 💻 cs.CY

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Alexander K. Saeri , Jess Graham , Michael Noetel , Peter Slattery , Dennis Ah-king , Edla Aittokallio , Ibitola Akindehin , Abbas Al Mahdi
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Elie Alhajjar Rafael Andersson Lipcsey Gary Ang Catherine M. Azam Amos Azaria Rishal Balkissoon Isabel Barber\'a 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 Gipi\v{s}kis 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 Matthew J. McMahon Minas Megalokonomos Nicolas Mo\"es Fernando Mourao Yaroslav Mukhin Malcolm Murray Simon Mylius Neeraj Nagpal Koichi Nakada Anna Neumann Jessica Newman Kwan Yee Ng Minh N. Nguyen Quynh Phuong Nguyen Se\'an S. \'O h\'Eigeartaigh Daria Onitiu Kelly Onu Oscar Oviedo-Trespalacios Ugur Ozer Chanwoo Park M. Alejandra Parra-Orlandoni Patricia Paskov Anna M. Pastwa Burak Piskin Jacob Pratt Claudiu A. Predincea Marjana Prifti Skenduli Kenneth Priore Mukunda Madhab Pujari Zhenting Qi Preethi Raghunathan Robi Rahman Deepika Raman Max Reddel Jyoti Ruparel Emma B. Ruttkamp-Bloem Tiffany Saade Greg Sadler Said Saillant Paul M. Salmon Ayrton San Joaquin Lama Saouma Maziya Sarangpurwala Supheakmungkol Sarin Daniel S. Schiff Anna D. Schilling Chris Schmitz Reva Schwartz Abeer Sharma Tianhao Shen Kehan Sheng Maury D. Shenk Eli Sherman Chandler Smith Julie M. Smith Estevenson Solano Oliver Sourbut Madhulika Srikumar Ryan Stendall Jakob Stenseke Michael Stern Joshua Sternfeld Nikko Stevens Ilia Sucholutsky Yuanyuan Sun Mariami Tkeshelashvili Cristian Trout Brian Tse Nikolaos Tsinganos Michelle Vaccaro Anthony R. Valiaveedu Ramakrishnan Veeramony Jeremy Verdo Pulkit Verma Andrea Luigi Vitali Jinge Wang JR Washebek Yonah Welker George F. Westerman James Williams Tristan Williams Rongwu Xu Mick Yang Xuemeng Yang Sander Zeijlemaker Jingyu Zhang Marta Ziosi Neil Thompson
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classification 💻 cs.CY
keywords risksexpertscatastrophicjudgedoutcomesprioritizationprobabilityresponsibility
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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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