A Definition of General-Purpose AI Systems: Mitigating Risks from the Most Generally Capable Models

8 Pages Posted: 25 Apr 2023 Last revised: 16 Jul 2023

Date Written: April 19, 2023


The European Union (EU) is currently going through the legislative process on the EU AI Act - the
first bill intended to regulate Artificial Intelligence (AI) comprehensively in a major jurisdiction. The
bill includes provisions to manage risks of generally capable AIs classified as "General Purpose AI
Systems" (GPAIS). We believe that this crucial aspect of the act could be improved by focusing the
definition more on the most generally capable systems, which bring very specific risks. The Future
of Life Institute (FLI) proposed a definition of GPAIS to better target these models, a significant
step in the right direction. Expanding on FLI’s proposal, this paper introduces a new definition of
GPAIS, which serves to clearly differentiate between narrow and general systems, and cannot be
easily exploited by GPAIS providers who may wish to avoid new regulatory constraints.

This paper consists of two sections. The first section discusses the specific risks of GPAIS, including unpredictability, adaptability, and the potential for emergent capabilities. The second section presents the new definition of GPAIS, and explains the changes made and how they address the risks presented in the first section. Our final definition for GPAIS is the following: "An AI system that can accomplish a range of distinct valuable tasks, including some for which it was not specifically trained."

The EU AI Act could set a global standard for AI-related risk management. The aim of this document is to help inform AI Act draft reviews and improve the ability to mitigate risks from the most generally capable models to protect stakeholders in the EU and globally.

Keywords: Artificial intelligence, AI risk management, European Union, governance of technology

Suggested Citation

Campos, Simeon and Laurent, Romain, A Definition of General-Purpose AI Systems: Mitigating Risks from the Most Generally Capable Models (April 19, 2023). Available at SSRN: https://ssrn.com/abstract=4423706 or http://dx.doi.org/10.2139/ssrn.4423706

Simeon Campos (Contact Author)

SaferAI ( email )


Romain Laurent


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