AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models

TL;DR

This paper presents a comprehensive profile of risk-management standards tailored for general-purpose AI and foundation models, aiming to guide developers in mitigating risks associated with these powerful AI systems.

Contribution

It introduces a structured risk-management profile specifically for GPAI and foundation models, building on existing standards and addressing unique challenges faced by developers.

Findings

Provides risk-management practices for GPAI and foundation models

Aligns with NIST AI Risk Management Framework and ISO standards

Aims to improve safety and reliability of large-scale AI systems

Abstract

Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the umbrella term 'GPAI/foundation models' except where greater specificity is needed), can provide many beneficial capabilities but also risks of adverse events with profound consequences. This document provides risk-management practices or controls for identifying, analyzing, and mitigating risks of GPAI/foundation models. We intend this document primarily for developers of large-scale, state-of-the-art GPAI/foundation models; others that can benefit from this guidance include downstream developers of end-use applications that build on a GPAI/foundation model. This document facilitates conformity with or use of leading AI risk management-related standards, adapting and building on the generic voluntary guidance in the NIST AI Risk Management Framework and ISO/IEC 23894, with a focus on the unique issues faced by developers of GPAI/foundation models.