I agree with the concern about accidentally making it harder for X-risk regulations to be passed—probably also something to keep in mind for the part of the community that works on mitigating the misuse of AI. Here are some concerns specifically to this point which I have and am curious what people think about it:
1. Policy Feasibility: Policymakers often operate on short-term electoral cycles, which inherently conflict with the long-term nature of x-risks. This temporal mismatch reduces the likelihood of substantial policy action. Therefore, advocacy strategies should focus on aligning x-risk mitigation with short-term political incentives.
2. Incrementalism as Bayesian Updating: A step-by-step regulatory approach can serve as real-world Bayesian updating. Initial, simpler policies can act as ‘experiments,’ the outcomes of which can inform more complex policies. This iterative process increases the likelihood of effective long-term strategies.
3. Balanced Multi-Tiered Regulatory Approach: Addressing immediate societal concerns or misuse (like deep fakes) seems necessary to any sweeping AI x-risk regulation since it seems to be in the Overton window and constituents’ minds. In such a scenario, it would require significant political or social capital to pass something only aimed at x-risks but not about the other concerns.
By establishing regulatory frameworks that address more immediate concerns based on multi-variate utility functions, we can probably lay the groundwork for more complex regulations aimed at existential risks. This is also why I think X-risk policy advocates come off as radical, robotic or “a bit out there”—they are so focused on talking about X-risk that they forget the more immediate or short-term human concerns.
With X-risk regulation, there doesn’t seem to be a silver bullet; these things will require intellectual rigour, pragmatic compromise and iterations themselves (also say hello to policy inertia).
I agree with the concern about accidentally making it harder for X-risk regulations to be passed—probably also something to keep in mind for the part of the community that works on mitigating the misuse of AI.
Here are some concerns specifically to this point which I have and am curious what people think about it:
1. Policy Feasibility: Policymakers often operate on short-term electoral cycles, which inherently conflict with the long-term nature of x-risks. This temporal mismatch reduces the likelihood of substantial policy action. Therefore, advocacy strategies should focus on aligning x-risk mitigation with short-term political incentives.
2. Incrementalism as Bayesian Updating: A step-by-step regulatory approach can serve as real-world Bayesian updating. Initial, simpler policies can act as ‘experiments,’ the outcomes of which can inform more complex policies. This iterative process increases the likelihood of effective long-term strategies.
3. Balanced Multi-Tiered Regulatory Approach: Addressing immediate societal concerns or misuse (like deep fakes) seems necessary to any sweeping AI x-risk regulation since it seems to be in the Overton window and constituents’ minds. In such a scenario, it would require significant political or social capital to pass something only aimed at x-risks but not about the other concerns.
By establishing regulatory frameworks that address more immediate concerns based on multi-variate utility functions, we can probably lay the groundwork for more complex regulations aimed at existential risks. This is also why I think X-risk policy advocates come off as radical, robotic or “a bit out there”—they are so focused on talking about X-risk that they forget the more immediate or short-term human concerns.
With X-risk regulation, there doesn’t seem to be a silver bullet; these things will require intellectual rigour, pragmatic compromise and iterations themselves (also say hello to policy inertia).