How to Distinguish Between Honest and Dishonest Arguments

This is an update on the work on AI Safety via Debate that we previously wrote about here. 

What we did: 

We tested the debate protocol introduced in AI Safety via Debate with human judges and debaters. We found various problems and improved the mechanism to fix these issues (details of these are in the appendix). However, we discovered that a dishonest debater can often create arguments that have a fatal error, but where it is very hard to locate the error. We don’t have a fix for this “obfuscated argument” problem, and believe it might be an important quantitative limitation for both IDA and Debate.

Key takeaways and relevance for alignment:

Our ultimate goal is to find a mechanism that allows us to learn anything that a machine learning model knows: if the model can efficiently find the correct answer to some problem, our mechanism should favor the correct answer while only requiring a tractable number of human judgements and a reasonable number of computation steps for the model. We’re working under a hypothesis that there are broadly two ways to know things: via step-by-step reasoning about implications (logic, computation…), and by learning and generalizing from data (pattern matching, bayesian updating…).

Original text:

https://www.alignmentforum.org/posts/PJLABqQ962hZEqhdB/debate-update-obfuscated-arguments-problem

Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.

---

A podcast by BlueDot Impact.

Learn more on the AI Safety Fundamentals website.