AI agents break rules under everyday pressure

Blameless postmortem culture recognizes human error as an inevitability and asks those with influence to design systems that maintain safety in the face of human error. In the software engineering world, this typically means automation, because while automation can and usually does have faults, it doesn't suffer from human error.

Now we've invented automation that commits human-like error at scale.

I wouldn't call myself anti-AI, but it does seem fairly obvious to me that directly automating things with AI will probably always have substantial risk and you have much more assurance, if you involve AI in the process, using it to develop a traditional automation. As a low-stakes personal example, instead of using AI to generate boilerplate code, I'll often try to use AI to generate a traditional code generator to convert whatever DSL specification into the chosen development language source code, rather than asking AI to generate the development language source code directly from the DSL.

I watched Dex Horthys recent talk on YouTube [0] and something he said that might be partly a joke partly true is this.

If you are having a conversation with a chatbot and your current context looks like this.

You: Prompt

AI: Makes mistake

You: Scold mistake

AI: Makes mistake

You: Scold mistake

Then the next most likely continuation from in context learning is for the AI to make another mistake so you can Scold again ;)

I feel like this kind of shenanigans is at play with this stuffing the context with roleplay.

[0] https://youtu.be/rmvDxxNubIg?si=dBYQYdHZVTGP6Rvh

I tried to think about how we might (in the EU) start to think about this problem within the law, if of interest to anyone: https://www.europeanlawblog.eu/pub/dq249o3c/release/1

CMIIW currently AI models operate in two distinct modes:

1. Open mode during learning, where they take everything that comes from the data as 100% truth. The model freely adapts and generalizes with no constraints on consistency.

2. Closed mode during inference, where they take everything that comes from the model as 100% truth. The model doesn't adapt and behaves consistently even if in contradiction with the new information.

I suspect we need to run the model in the mix of the two modes, and possibly some kind of "meta attention" (epistemological) on which parts of the input the model should be "open" (learn from it) and which parts of the input should be "closed" (stick to it).

[dupe] https://news.ycombinator.com/item?id=46045390

I wonder who could have possibly predicted this being a result of using scraped web forums and Reddit posts for your training material.

Sure,

LLMs are trained on human behavior as exhibited on the Internet. Humans break rules more often under pressure and sometimes just under normal circumstances. Why wouldn't "AI agents" behave similarly?

The one thing I'd say is that humans have some idea which rules in particular to break while "agents" seem to act more randomly.

..because it's in their training data? Case closed

“AI agents: They're just like us”