AI Safety Fundamentals: Alignment

Challenges in Evaluating AI Systems

Apr 7, 2024

Exploring challenges in evaluating AI systems, the podcast delves into limitations of current evaluation suites and offers policy recommendations. Topics include pitfalls of using MM LU metric, difficulties in measuring social biases, hurdles in Bias Benchmark Task, complexities of Big Bench framework, and methodologies for red teaming in security evaluations.

22:33

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Quick takeaways

Challenges exist in accurately measuring AI model performance with multiple choice evaluations like MM LU and BBQ.

Practical implementation hurdles are faced by third-party frameworks like Big Bench and Helm in effectively evaluating AI systems.

Deep dives

Challenges in Multiple Choice Evaluations

Multiple choice evaluations like MM LU and BBQ pose challenges in accurately measuring model performance. MM LU's widespread use can lead to cheating through question familiarity, formatting changes impacting scores, inconsistent implementation by labs, and errors in proofreading. BBQ, measuring social biases, requires significant engineering effort, with unique challenges in scale, implementation time, and defining bias scores. Both evaluations highlight the complexities and potential inaccuracies in assessing AI models.

Introduction

3min

Challenges in Evaluating AI Systems Using MM LU and Measuring Social Biases

2min

Challenges in Evaluating AI Systems

5min

Evaluating AI Systems and Red Teaming for Security

8min

Challenges in Evaluating AI Systems

5min

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.

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AI Safety Fundamentals: Alignment

Challenges in Evaluating AI Systems

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Challenges in Multiple Choice Evaluations

Issues with Third-Party Evaluation Frameworks

Human and Expert Evaluations for National Security Threats

Remember Everything You Learn from Podcasts