www.sfgate.com/entertainmen...

Work with amazing folks: Lucy He, Nimra Nadeem, Michel Liao, Howard Chen, Danqi Chen, & Mariano-Florentino Cuéllar @carnegieendowment.org

Basically, if we’re going to take model specs/constitutional AI seriously, we need to optimize rules and build out surrounding consistency-enhancing structures, paralleling the legal system.

Let's build better natural language laws and law-following AI together! If interested, reach out!

Obviously, lots more to do in this space! I'm super excited about this direction and the forthcoming work that we're building out.

3️⃣ These computational tools, we think, can also be applied to positive models of the legal system, something that we’re tackling. More on this soon!

2️⃣ We leverage interpretive constraints or ambiguity to induce more consistent interpretations and debug laws for AI. These computational tools allow us to not only build more rigorous laws for AI, but adds a layer of visibility on what can go wrong, ex ante.

A few quick takeaways below, but I’ll drop more findings soon on this dense paper:

1️⃣ Given the same set of rules, models will interpret scenarios wildly differently. This gives us a mechanism to quantify interpretive ambiguity.

We model a space of reasonable interpreters and then modify rules, or add interpretive constraints, to reduce the entropy of the distribution.

Check out our new work, Statutory Construction and Interpretation for Artificial Intelligence, doing exactly this!

Paper: arxiv.org/abs/2509.01186
Policy Brief: www.polarislab.org/briefs/Statu...
Blog: www.polarislab.org#/blog/statut...