The PPOu Framework: A Structured Approach for Assessing the Likelihood of Malicious Use of Advanced AI Systems

The diffusion of increasingly capable AI systems has produced concern that bad actors could intentionally misuse current or future AI systems for harm. Governments have begun to create new entities—such as AI Safety Institutes—tasked with assessing these risks. However, approaches for risk assessment are currently fragmented and would benefit from broader disciplinary expertise. As it stands, it is often unclear whether concerns about malicious use misestimate the likelihood and severity of the risks. This article advances a conceptual framework to review and structure investigation into the likelihood of an AI system (X) being applied to a malicious use (Y). We introduce a three-stage framework of (1) Plausibility (can X be used to do Y at all?), (2) Performance (how well does X do Y?), and (3) Observed use (do actors use X to do Y in practice?). At each stage, we outline key research questions, methodologies, benefits and limitations, and the types of uncertainty addressed. We also offer ideas for directions to improve risk assessment moving forward.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31653
Citations
2
Primary Topic
Terrorism, Counterterrorism, and Political Violence
Type
article
Field-Weighted Citation Impact
2.26
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article

The PPOu Framework: A Structured Approach for Assessing the Likelihood of Malicious Use of Advanced AI Systems

Girish Sastry, Josh A. Goldstein
2 citations
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Terrorism, Counterterrorism, and Political Violence
2.26
article

The PPOu Framework: A Structured Approach for Assessing the Likelihood of Malicious Use of Advanced AI Systems

Girish Sastry, Josh A. Goldstein
article en
2 citations

Abstract

The diffusion of increasingly capable AI systems has produced concern that bad actors could intentionally misuse current or future AI systems for harm. Governments have begun to create new entities—such as AI Safety Institutes—tasked with assessing these risks. However, approaches for risk assessment are currently fragmented and would benefit from broader disciplinary expertise. As it stands, it is often unclear whether concerns about malicious use misestimate the likelihood and severity of the risks. This article advances a conceptual framework to review and structure investigation into the likelihood of an AI system (X) being applied to a malicious use (Y). We introduce a three-stage framework of (1) Plausibility (can X be used to do Y at all?), (2) Performance (how well does X do Y?), and (3) Observed use (do actors use X to do Y in practice?). At each stage, we outline key research questions, methodologies, benefits and limitations, and the types of uncertainty addressed. We also offer ideas for directions to improve risk assessment moving forward.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Georgetown University (US), Center for Security and Emerging Technology (US), OpenAI (United States) (US)
Openalex Percentile: Top 9%
Terrorism, Counterterrorism, and Political Violence
2.26
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The PPOu Framework: A Structured Approach for Assessing the Likelihood of Malicious Use of Advanced AI Systems — Girish Sastry, Josh A. Goldstein · Proceedings of the AAAI/ACM Conference on AI Ethics and Society (2024) | TGRS Research Map | TGRS