Back
Frontier Model Forum - Issue Brief: Preliminary Taxonomy of Pre-Deployment Frontier AI Safety Evaluations
webCredibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: Frontier Model Forum
Published by the Frontier Model Forum, this brief is a key industry document for understanding how leading AI labs conceptualize and categorize pre-deployment safety evaluations, relevant to policy discussions and evaluation methodology development.
Metadata
Importance: 68/100policy briefreference
Summary
The Frontier Model Forum presents a structured taxonomy of safety evaluations that frontier AI developers should conduct before deploying models, covering categories like dangerous capabilities, alignment, and societal risks. It aims to standardize evaluation practices across major AI labs and inform policy discussions around responsible deployment. The brief reflects industry-led efforts to operationalize safety commitments made by leading developers.
Key Points
- •Proposes a taxonomy organizing pre-deployment safety evals into categories such as dangerous capabilities, model alignment, and broader societal harms.
- •Developed by the Frontier Model Forum, a consortium of major AI labs (Anthropic, Google, Microsoft, OpenAI), reflecting industry self-governance efforts.
- •Aims to create shared vocabulary and standards for safety evaluation across frontier AI developers to improve consistency and accountability.
- •Intended to inform policymakers and regulators seeking to understand what responsible pre-deployment evaluation looks like in practice.
- •Acknowledges evaluations are preliminary and evolving, recognizing the field of AI safety evals lacks mature, standardized methodologies.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Frontier Model Forum | Organization | 58.0 |
Cached Content Preview
HTTP 200Fetched Mar 20, 202612 KB
[Skip to content](https://www.frontiermodelforum.org/updates/issue-brief-preliminary-taxonomy-of-pre-deployment-frontier-ai-safety-evaluations/#wp--skip-link--target)
## Issue Brief: Preliminary Taxonomy of Pre-Deployment Frontier AI Safety Evaluations
By:
Frontier Model Forum
Posted on:
20th December 2024
As frontier AI systems continue to advance, rigorous and scientifically grounded safety evaluations will be increasingly essential. Although frontier AI holds immense promise for society, the growing capabilities of advanced AI systems may also introduce risks to public safety and security. Ensuring such systems benefit society without compromising safety will depend on the development of robust mechanisms for identifying and mitigating potential harms. Safety evaluations, which aim to measure one or more safety-relevant capabilities or behaviors of a model or system, are a key mechanism by which model or system risks are assessed more broadly.
A cohesive evaluation ecosystem for frontier AI systems will be critical to their safe and responsible development. Yet current evaluations for frontier AI models and systems differ substantially in their methods, purpose, and terminology. Establishing a shared understanding of the functions and types of evaluations is a key first step toward building a more effective ecosystem. This is especially true for safety evaluations that are carried out before a model or system is released and face different constraints than post-deployment evaluations focused on user impacts.
This issue brief offers an initial high-level taxonomy of pre-deployment safety evaluations for frontier AI models and systems. Based on the public literature as well as input from safety experts across the Frontier Model Forum, the brief is part of a broader workstream that aims to inform public discussion of best practices for AI safety evaluations.
**Recommended Taxonomy**
As opposed to more commercially-focused evaluations, which typically focus on performance metrics, safety evaluations aim to assess the potential risks of a given frontier AI model or system whose capabilities could be misused to cause harm or could lead to unintended harm. Risks refer to outcomes that are not considered desirable (or even intentional) and can lead to negative impacts on users, groups, entities, systems, or societies, and that may arise as a result of the behaviors or capabilities of an AI model or system.[1](https://www.frontiermodelforum.org/updates/issue-brief-preliminary-taxonomy-of-pre-deployment-frontier-ai-safety-evaluations/#c016d283-c909-437c-bda8-7760c8f23b08)
_Methodology_
Safety evaluations can be distinguished in terms of methodology. For evaluations of AI models or systems themselves, two common methods include:
- **Benchmark evaluations**. Benchmark evaluations are focused on quantifying the capabilities of a model in terms of standardized criteria and in such a way that the results can be compared at scale, over time, and
... (truncated, 12 KB total)Resource ID:
9ead95e40d74341b | Stable ID: ZWRhOWM5ND