Mandate specific architectures to be used because the government is more familiar with them, even if other architectures would be safer.
Mandate specific “alignment” protocols to be used that do not, in fact, make an AI safer or more legible, and divert resources to them that would otherwise have gone to actual alignment work.
Declare certain AI “biases” unacceptable, and force the use of AIs that do not display them. If some of these “biases” are in fact real patterns about the world, this could select for AIs with unpredictable blind spots and/or deceptive AIs.
Increase compliance costs such that fewer people are willing to work on alignment, and smaller teams might be forced out of the field entirely.
Subsidize unhelpful approaches to alignment, drawing in people more interested in making money than in actually solving the problem, increasing the noise-to-signal ratio.
Create licensing requirements that force researchers out of the field.
Create their own AI project under political administrators that have no understanding of alignment, and no real interest in solving it, thereby producing AIs that have an unusually high probability of causing doom and an unusually low probability of producing useful alignment research and/or taking a pivotal act to reduce or end the risk.
Push research underground, reducing the ability of researchers to collaborate.
Push research into other jurisdictions with less of a culture of safety. E.g. DeepMind cares enough about alignment to try to quantify how hard a goal can be optimized before degenerate behavior emerges; if they are shut down and some other organization elsewhere takes the lead, they may well not share this goal.
This was just off the top of my head. In real life, regulation tends to cause problems that no one saw coming in advance. The strongest counterargument here is that regulation should at least slow capabilities research down, buying more time for alignment. But regulators do not have either the technical knowledge or the actual desire to distinguish capabilities and alignment research, and alignment research is much more fragile.
Mandate specific architectures to be used because the government is more familiar with them, even if other architectures would be safer.
Mandate specific “alignment” protocols to be used that do not, in fact, make an AI safer or more legible, and divert resources to them that would otherwise have gone to actual alignment work.
Declare certain AI “biases” unacceptable, and force the use of AIs that do not display them. If some of these “biases” are in fact real patterns about the world, this could select for AIs with unpredictable blind spots and/or deceptive AIs.
Increase compliance costs such that fewer people are willing to work on alignment, and smaller teams might be forced out of the field entirely.
Subsidize unhelpful approaches to alignment, drawing in people more interested in making money than in actually solving the problem, increasing the noise-to-signal ratio.
Create licensing requirements that force researchers out of the field.
Create their own AI project under political administrators that have no understanding of alignment, and no real interest in solving it, thereby producing AIs that have an unusually high probability of causing doom and an unusually low probability of producing useful alignment research and/or taking a pivotal act to reduce or end the risk.
Push research underground, reducing the ability of researchers to collaborate.
Push research into other jurisdictions with less of a culture of safety. E.g. DeepMind cares enough about alignment to try to quantify how hard a goal can be optimized before degenerate behavior emerges; if they are shut down and some other organization elsewhere takes the lead, they may well not share this goal.
This was just off the top of my head. In real life, regulation tends to cause problems that no one saw coming in advance. The strongest counterargument here is that regulation should at least slow capabilities research down, buying more time for alignment. But regulators do not have either the technical knowledge or the actual desire to distinguish capabilities and alignment research, and alignment research is much more fragile.
Thanks, some of those possibilities do seem quite risky and I hadn’t thought about them before.