Challenges in Evaluating AI Systems

AI Safety Fundamentals: Alignment

Most conversations around the societal impacts of artificial intelligence (AI) come down to discussing some quality of an AI system, such as its truthfulness, fairness, potential for misuse, and so on. We are able to talk about these characteristics because we can technically evaluate models for their performance in these areas. But what many people working inside and outside of AI don’t fully appreciate is how difficult it is to build robust and reliable model evaluations. Many of today’s existing evaluation suites are limited in their ability to serve as accurate indicators of model capabilities or safety.
At Anthropic, we spend a lot of time building evaluations to better understand our AI systems. We also use evaluations to improve our safety as an organization, as illustrated by our Responsible Scaling Policy. In doing so, we have grown to appreciate some of the ways in which developing and running evaluations can be challenging.

Here, we outline challenges that we have encountered while evaluating our own models to give readers a sense of what developing, implementing, and interpreting model evaluations looks like in practice.
Source:
https://www.anthropic.com/news/evaluating-ai-systems
Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

Capitole

1. Challenges in Evaluating AI Systems (00:00:00)

2. Introduction (00:00:15)

3. Challenges (00:02:23)

4. The supposedly simple multiple-choice evaluation (00:02:25)

5. One size doesn't fit all when it comes to third-party evaluation frameworks (00:06:42)

6. The subjectivity of human evaluations (00:10:45)

7. The ouroboros of model-generated evaluations (00:15:29)

8. Preserving the objectivity of third-party audits while leveraging internal expertise (00:16:56)

9. Policy recommendations (00:18:44)

10. Conclusion (00:21:50)