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

2026-06-03

AI Summary

This study addresses the urgent need for systematic assessment of artificial intelligence (AI) risks to clarify their severity, vulnerable targets, and responsible stakeholders. Through a three-round Delphi survey involving 272 international AI experts, it presents the first quantitative prioritization of 24 AI risks across multiple dimensions—including likelihood, potential harm, vulnerability, and attribution of responsibility. The analysis identifies the five most critical risks expected to emerge within the next five years and reveals that, even with mitigation measures in place, five risks retain high probabilities of catastrophic outcomes. Under baseline scenarios, 18 risks exhibit a greater than 10% chance of leading to disaster. These findings provide a foundation for assigning governance responsibilities and issuing early warnings for high-risk domains in AI development and deployment.

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.

Problem

• AI risks
• risk prioritization
• catastrophic outcomes
• vulnerability
• responsibility

Innovation

• Delphi study
• AI risk prioritization
• catastrophic outcomes
• responsibility attribution
• expert consensus