23 low-regret recommendations for AI policy

A few days ago, I published a guest post by Tim Fist and Saif Khan of the Institute for Progress, discussing the question of whether we should deliberately try to slow down the rate of AI progress:

Should we "pace" AI self-improvement? Tim Fist and Saif M. Khan · Aug 9 Read full story

The authors promised a raft of specific policy recommendations, and they didn’t disappoint. Here is part 2, with all of those recommendations. Did you know that 23 is my lucky number?

In our last post, we evaluated the claims of a recent open letter by AI company employees calling for governments to “pace” frontier AI development. To summarize:

• Rapid progress towards fully automated AI R&D has empirical support, but it’s less clear how much it will accelerate AI capabilities or pose severe risks.

• Despite substantial uncertainty, we believe some preparatory policy action is warranted. This follows both from how serious the possible direct risks are and the risk that political backlash to AI-driven disruptions results in poorly-reasoned policy measures, such as broad bans on new data centers.

• If “pacing” becomes necessary, we think it should consist of two parts: first, specifying thresholds for when automated AI R&D is likely to pose severe risks; and second, if a threshold is exceeded, incentivizing AI companies to reallocate resources away from the most risky research, and towards activities that make further automation safer, or diffuse the benefits of existing AI faster.

Without preparation now, however, our preferred pacing strategy will be impossible to implement. In this post, we’ll describe how the US can concretely prepare for the further automation of AI R&D and the risks it entails.

Still, we aren’t certain whether the benefits of pacing outweigh the downsides, especially given the risk that government regulation is implemented counterproductively. So to make policy preparation as targeted and low-regret as possible, we think any intervention should meet the following five criteria:

1. Target only AI development activities that could lead to serious and irreversible harms.

2. Minimize any slowdown in the diffusion of existing AI capabilities, and ideally accelerate it.

3. Impose low costs, or deliver clear benefits, even if automated AI R&D and its attendant risks prove unlikely.

4. Avoid systematically disadvantaging more cautious labs and countries.

5. Avoid establishing a new regulatory apparatus that is likely to be misused (e.g., by concentrating power in a small set of companies).

A surprisingly wide range of policy moves meet these criteria. We’ve identified 23 of them, and they span 7 areas:

• Transparency: giving the government and public more visibility into automated AI R&D

• State capacity: improving the government̵