There are many ways to approach these adaptations. Each of us can choose where to put our efforts. If you believe in a laissez-faire approach to AI regulation, you can focus on one of the areas of society’s adaptation. If you believe society shouldn’t change because of AI, you can focus on how AI should adapt to society. Efforts in all areas and focusing on the whole range of possible responses are needed.

It is not possible to predict, schedule, etc., an invention or innovation. On the other hand, it is possible to foster it. Fostering innovation in public policy responses to the AI adoption challenges we face is exactly what we need.

A good first step is to create well-articulated scenarios for each challenge and possible responses. The plural of scenario is important here.  We are going into uncharted waters and therefore can’t predict with accuracy what exactly will happen. Thus, we need to at least consider optimistic, pessimistic, and middle-of-the-road scenarios for each area of adaptation.  There may also be additional parameters we need to develop scenarios for. With the scenarios in front of us, we can begin to develop responses appropriate for each. This doesn’t guarantee innovation. But it does provide a foundation for it.

The effectiveness of scenarios was well demonstrated by the AI 2027 effort. Over time, competitive pressures have diluted the self-regulation it fostered. Therefore, what we learned is that good scenario sets need to be created for each of the adaptation areas. They need to be constantly refreshed and kept in the public eye. Then, a real effort at practical public policy to deal with them must be created. Such efforts must take into account different cultural, political, and development situations around the world. For example, the effects of AI are likely to be very different and require different public policies in subsistence farming areas and highly developed economies.

To accompany the scenarios, there needs to be safe fora where people from a broad background can come together and discuss the scenarios and possible public policy responses. Safe means that individuals should not fear reprisal for anything that they say. Participants should include people well-versed in AI technology, economics, anthropology, sociology, political science, etc. One such group is the AIWG.  Another is the HAI project at Stanford University. There are a growing number of these around the world, and they need to be encouraged and supported.

Businesses need to be involved in these conversations. Doing so in the proper way may be difficult. It is all too easy for people representing businesses to fall back into a lobbying mode that only seeks to capture a very short-term advantage for their company. A kind of ‘my company wins, and I don’t care if everybody else loses’. The challenge we face is so broad and deep that only public policies that produce the broadest possible good can be effective.

Public policy innovation is required. However, it is not effective without political action to implement it. So, readers talk about these issues with their friends. It is the sense that everybody's talking about it that gets the political system moving.

AI public policy is trailing AI’s rapid technological progress. That needs to change. Having a range of scenarios to discuss and safe places to have those conversations is the best way to encourage the public policy innovation we need.