Part of the reason that enforcement works, though, is that human agents have an independent incentive not to break the law (or, e.g., report legal violations) since they are legally accountable for their actions.
Certainly you still need legal accountability—why wouldn’t we have that? If we solve alignment, then we can just have the AI’s owner be accountable for any law-breaking actions the AI takes.
This seems to require the same type of fundamental ML research that I am proposing: mapping AI actions onto laws.
Imagine trying to make teenagers law-abiding. You could have two strategies:
1. Rewire the neurons or learning algorithm in their brain such that you can say “the computation done to produce the output of neuron X reliably tracks whether a law has been violated, and because of its connection via neuron Y to neuron Z, if an action is predicted to violate a law, the teenager won’t take it”.
2. Explain to them what the laws are (relying on their existing ability to understand English, albeit fuzzily), and give them incentives to follow it.
I feel much better about 2 than 1.
When you say “programming AI to follow law” I imagine case 1 above (but for AI systems instead of humans). Certainly the OP seemed to be arguing for this case. This is the thing I think is extremely difficult.
I am much happier about AI systems learning about the law via case 2 above, which would enable the AI police applications I mentioned above.
However, some ML people I have talked about this with have given positive feedback, so I think you might be overestimating the difficulty.
I suspect they are thinking about case 2 above? Or they might be thinking of self-driving car type applications where you have an in-code representation of the world? Idk, I feel confident enough of this that I’d predict that there is a miscommunication somewhere, rather than an actual strong difference of opinion between me and them.
Certainly you still need legal accountability—why wouldn’t we have that? If we solve alignment, then we can just have the AI’s owner be accountable for any law-breaking actions the AI takes.
I agree that that is a very good and desirable step to take. However, as I said, it also incentives the AI-agent to obfuscate its actions and intentions to save its principal. In the human context, human agents do this but are independently disincentivized from breaking the law they face legal liability (a disincentive) for their actions. I want (and I suspect you also want) AI systems to have such incentivization.
If I understand correctly, you identify two ways to do this in the teenager analogy:
Rewiring
Explaining laws and their consequences and letting the agent’s existing incentives do the rest.
I could be wrong about this, but ultimately, for AI systems, it seems like both are actually similarly difficult. As you’ve said, for 2. to be most effective, you probably need “AI police.” Those police will need a way of interpreting the legality of an AI agent’s {”mental” state; actions} and mapping them only existing laws.
But if you need to do that for effective enforcement, I don’t see why (from a societal perspective) we shouldn’t just do that on the actor’s side and not the “police’s” side. Baking the enforcement into the agents has the benefits of:
Not incentivizing an arms race
Giving the enforcer’s a clearer picture of the AI’s “mental state”
I want (and I suspect you also want) AI systems to have such incentivization.
Not obviously. My point is just that if the AI is aligned with an human principal, and that human principal can be held accountable for the AI’s actions, then that automatically disincentivizes AI systems from breaking the law.
(I’m not particularly opposed to AI systems being disincentivized directly, e.g. by making it possible to hold AI systems accountable for their actions. It just doesn’t seem necessary in the world where we’ve solved alignment.)
I don’t see why (from a societal perspective) we shouldn’t just do that on the actor’s side and not the “police’s” side.
I agree that doing it on the actor’s side is better if you can ensure it for all actors, but you have to also prevent the human principal from getting a different actor that isn’t bound by law.
E.g. if you have a chauffeur who refuses to exceed the speed limit (in a country where the speed limit that’s actually enforced is 10mph higher), you fire that chauffeur and find a different one.
(Also, I’m assuming you’re teaching the agent to follow the law via something like case 2 above, where you have it read the law and understand it using its existing abilities, and then train it somehow to not break the law. If you were instead thinking something like case 1, I’d make the second argument that it isn’t likely to work.)
Imagine trying to make teenagers law-abiding. You could have two strategies:
1. Rewire the neurons or learning algorithm in their brain such that you can say “the computation done to produce the output of neuron X reliably tracks whether a law has been violated, and because of its connection via neuron Y to neuron Z, if an action is predicted to violate a law, the teenager won’t take it”.
2. Explain to them what the laws are (relying on their existing ability to understand English, albeit fuzzily), and give them incentives to follow it.
I feel much better about 2 than 1.
What if they also have access to nukes or other weapons that could prevent them or their owners from being held accountable if they’re used?
EDIT: Hmm, maybe they need strong incentives to check in with law enforcement periodically? This would be bounded per interval of time, and also (much) greater in absolute sign than any other reward they could get per period.
What if they also have access to nukes or other weapons that could prevent them or their owners from being held accountable if they’re used?
I’m going to interpret this as:
Assume that the owners are misaligned w.r.t the rest of humanity (controversial, to me at least).
Assume that enforcement is impossible.
Under these assumptions, I feel better about 1 than 2, in the sense that case 1 feels like a ~5% chance of success while case 2 feels like a ~0% chance of success. (Numbers made up of course.)
But this seems like a pretty low-probability way the world could be (I would bet against both assumptions), and the increase in EV from work on it seems pretty low (since you only get 5% chance of success), so it doesn’t seem like a strong argument to focus on case 1.
Assume that the owners are misaligned w.r.t the rest of humanity (controversial, to me at least).
Couldn’t the AI end up misaligned with the owners by accident, even if they’re aligned with the rest of humanity? The question is whether 1 or 2 is better at aligning the AI in cases where enforcement is impossible or explicitly prevented.
I edited my comment above before I got your reply to include the possibility of the AI being incentivized to ensure it gets monitored by law enforcement. Its reward function could look like
f(x)+∞∑i=1IMi(x)
where f is bounded to have a range of length ≤1, and IMi(x) is 1 if the AI is monitored by law enforcement in period i (and passes some test) and 0 otherwise. You could put an upper bound on the number of periods or use discounting to ensure the right term can’t evaluate to infinity since that would allow f to be ignored (maybe the AI will predict its expected lifetime to be infinite), but this would eventually allow f to overcome the IMi.
Couldn’t the AI end up misaligned with the owners by accident, even if they’re aligned with the rest of humanity?
Yes, but as I said earlier, I’m assuming the alignment problem has already been solved when talking about enforcement. I am not proposing enforcement as a solution to alignment.
If you haven’t solved the alignment problem, enforcement doesn’t help much, because you can’t rely on your AI-enabled police to help catch the AI-enabled criminals, because the police AI itself may not be aligned with the police.
The question is whether 1 or 2 is better at aligning the AI in cases where enforcement is impossible or explicitly prevented.
Case 2 is assuming that you already have an intelligent agent with motivations, and then trying to deal with that after the fact. I agree this is not going to work for alignment. If for some reason I could only do 1 or 2 for alignment, I would try 1. (But there are in fact a bunch of other things that you can do.)
Certainly you still need legal accountability—why wouldn’t we have that? If we solve alignment, then we can just have the AI’s owner be accountable for any law-breaking actions the AI takes.
Imagine trying to make teenagers law-abiding. You could have two strategies:
1. Rewire the neurons or learning algorithm in their brain such that you can say “the computation done to produce the output of neuron X reliably tracks whether a law has been violated, and because of its connection via neuron Y to neuron Z, if an action is predicted to violate a law, the teenager won’t take it”.
2. Explain to them what the laws are (relying on their existing ability to understand English, albeit fuzzily), and give them incentives to follow it.
I feel much better about 2 than 1.
When you say “programming AI to follow law” I imagine case 1 above (but for AI systems instead of humans). Certainly the OP seemed to be arguing for this case. This is the thing I think is extremely difficult.
I am much happier about AI systems learning about the law via case 2 above, which would enable the AI police applications I mentioned above.
I suspect they are thinking about case 2 above? Or they might be thinking of self-driving car type applications where you have an in-code representation of the world? Idk, I feel confident enough of this that I’d predict that there is a miscommunication somewhere, rather than an actual strong difference of opinion between me and them.
I agree that that is a very good and desirable step to take. However, as I said, it also incentives the AI-agent to obfuscate its actions and intentions to save its principal. In the human context, human agents do this but are independently disincentivized from breaking the law they face legal liability (a disincentive) for their actions. I want (and I suspect you also want) AI systems to have such incentivization.
If I understand correctly, you identify two ways to do this in the teenager analogy:
Rewiring
Explaining laws and their consequences and letting the agent’s existing incentives do the rest.
I could be wrong about this, but ultimately, for AI systems, it seems like both are actually similarly difficult. As you’ve said, for 2. to be most effective, you probably need “AI police.” Those police will need a way of interpreting the legality of an AI agent’s {”mental” state; actions} and mapping them only existing laws.
But if you need to do that for effective enforcement, I don’t see why (from a societal perspective) we shouldn’t just do that on the actor’s side and not the “police’s” side. Baking the enforcement into the agents has the benefits of:
Not incentivizing an arms race
Giving the enforcer’s a clearer picture of the AI’s “mental state”
Not obviously. My point is just that if the AI is aligned with an human principal, and that human principal can be held accountable for the AI’s actions, then that automatically disincentivizes AI systems from breaking the law.
(I’m not particularly opposed to AI systems being disincentivized directly, e.g. by making it possible to hold AI systems accountable for their actions. It just doesn’t seem necessary in the world where we’ve solved alignment.)
I agree that doing it on the actor’s side is better if you can ensure it for all actors, but you have to also prevent the human principal from getting a different actor that isn’t bound by law.
E.g. if you have a chauffeur who refuses to exceed the speed limit (in a country where the speed limit that’s actually enforced is 10mph higher), you fire that chauffeur and find a different one.
(Also, I’m assuming you’re teaching the agent to follow the law via something like case 2 above, where you have it read the law and understand it using its existing abilities, and then train it somehow to not break the law. If you were instead thinking something like case 1, I’d make the second argument that it isn’t likely to work.)
What if they also have access to nukes or other weapons that could prevent them or their owners from being held accountable if they’re used?
EDIT: Hmm, maybe they need strong incentives to check in with law enforcement periodically? This would be bounded per interval of time, and also (much) greater in absolute sign than any other reward they could get per period.
I’m going to interpret this as:
Assume that the owners are misaligned w.r.t the rest of humanity (controversial, to me at least).
Assume that enforcement is impossible.
Under these assumptions, I feel better about 1 than 2, in the sense that case 1 feels like a ~5% chance of success while case 2 feels like a ~0% chance of success. (Numbers made up of course.)
But this seems like a pretty low-probability way the world could be (I would bet against both assumptions), and the increase in EV from work on it seems pretty low (since you only get 5% chance of success), so it doesn’t seem like a strong argument to focus on case 1.
Couldn’t the AI end up misaligned with the owners by accident, even if they’re aligned with the rest of humanity? The question is whether 1 or 2 is better at aligning the AI in cases where enforcement is impossible or explicitly prevented.
I edited my comment above before I got your reply to include the possibility of the AI being incentivized to ensure it gets monitored by law enforcement. Its reward function could look like
where f is bounded to have a range of length ≤1, and IMi(x) is 1 if the AI is monitored by law enforcement in period i (and passes some test) and 0 otherwise. You could put an upper bound on the number of periods or use discounting to ensure the right term can’t evaluate to infinity since that would allow f to be ignored (maybe the AI will predict its expected lifetime to be infinite), but this would eventually allow f to overcome the IMi.
Yes, but as I said earlier, I’m assuming the alignment problem has already been solved when talking about enforcement. I am not proposing enforcement as a solution to alignment.
If you haven’t solved the alignment problem, enforcement doesn’t help much, because you can’t rely on your AI-enabled police to help catch the AI-enabled criminals, because the police AI itself may not be aligned with the police.
Case 2 is assuming that you already have an intelligent agent with motivations, and then trying to deal with that after the fact. I agree this is not going to work for alignment. If for some reason I could only do 1 or 2 for alignment, I would try 1. (But there are in fact a bunch of other things that you can do.)