A good way to regulate AI is to think of it as a complex adaptive system

livemint.com:

Last month, the Prime Minister’s Economic Advisory Council (PM-EAC) released a paper proposing a new approach to regulating Artificial Intelligence (AI). It argues that while our current approach of enacting reactionary regulations might work in a static, linear system with predictable risks, it is unlikely to work in the context of AI, which comprises emergent, non-linear systems. It argues that since AI is a dynamic network of diverse agents whose interactions generate emergent behaviours, we need to think of it as a complex adaptive system (CAS) and design regulations accordingly.

We already have experience dealing with complex adaptive systems like stock markets. The paper attempts to extract regulatory principles from those systems, so that we can apply them to AI. For instance, it suggests that we put in place guard-rails and partitions to define operational spaces within which AI can operate, so that, if needed, we can be sure it will not accidentally stray into potentially hazardous areas.

It also calls for building manual overrides and authorization choke-points directly into these AI systems, so that humans can effectively take control of operations where needed. It makes the case for “transparency and explainability," so that there will always be public scrutiny of these systems. The PM-EAC paper also suggests that we ensure “distinct accountability," so we can always identify the entity or individual responsible for any unintended consequence.