Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Rattachement africain : au, ca, lt, us, cn, ru. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
Où se fait cette recherche
-
The University of Queensland pays non établi dans la noticeUniversité ou école supérieure
-
Bank of Canada pays non établi dans la noticeInstitution
-
University of Waterloo pays non établi dans la noticeUniversité ou école supérieure
-
Vilnius University pays non établi dans la noticeUniversité ou école supérieure
-
Massachusetts Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
-
Tsinghua University pays non établi dans la noticeUniversité ou école supérieure
-
Cornell University pays non établi dans la noticeUniversité ou école supérieure
-
Concordia University Wisconsin pays non établi dans la noticeUniversité ou école supérieure
-
Concordia University pays non établi dans la noticeUniversité ou école supérieure
-
Moscow Institute of Thermal Technology pays non établi dans la noticeStructure de recherche
-
Harvard University Press pays non établi dans la noticeInstitution
-
University of Sydney The Anh Han School of Computing pays non établi dans la noticeUniversité ou école supérieure
The University of Queensland, Bank of Canada et University of Waterloo, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.