23 low-regret recommendations for AI policy

A few days ago, I shared a guest post discussing potential AI policy recommendations, specifically focusing on slowing down the rate of AI progress. The authors have provided a comprehensive list of 23 low-regret recommendations across seven areas to prepare for the automation of AI research and development (R&D) and associated risks.

These recommendations emphasize the importance of targeting only AI development activities that could result in serious and irreversible harms, while minimizing any slowdown in the diffusion of existing AI capabilities. The goal is to either accelerate or ensure the benefits of AI are widely distributed.

The proposed policies cover various aspects, such as transparency, state capacity, risk management, defensive and commercial AI use, and avoiding the establishment of new regulatory frameworks that could be misused.

For instance, one recommendation is to specify thresholds for when automated AI R&D might pose severe risks. Once a threshold is exceeded, it would incentivize AI companies to allocate resources towards making further automation safer or to disseminate the advantages of existing AI more rapidly.

These low-regret recommendations aim to provide targeted and low-risk policy interventions, ensuring that any regulatory actions are carefully considered and implemented with minimal potential downsides.