First, it would be hard to do. I am a programmer /â ML researcher and I have no idea how to program an AI to follow the law in some guaranteed way. I also have an intuitive sense that it would be very difficult. I think the vast majority of programmers /â ML researchers would agree with me on this.
This is valuable information. However, some ML people I have talked about this with have given positive feedback, so I think you might be overestimating the difficulty.
Second, it doesnât provide much value, because you can get most of the benefits via enforcement, which has the virtue of being the solution we currently use.
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.
But AI-enabled police would be able to probe actions, infer motives, and detect bad behavior better than humans could. In addition, AI systems could have fewer rights than humans, and could be designed to be more transparent than humans, making the policeâs job easier.
This seems to require the same type of fundamental ML research that I am proposing: mapping AI actions onto laws.
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 accountabilityâwhy wouldnât we have that? If we solve alignment, then we can just have the AIâs 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 âthe 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 wonât take itâ.
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 âprogramming AI to follow lawâ 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 Iâd predict that there is a miscommunication somewhere, rather than an actual strong difference of opinion between me and them.
Certainly you still need legal accountabilityâwhy wouldnât we have that? If we solve alignment, then we can just have the AIâs 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 agentâs 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 youâve said, for 2. to be most effective, you probably need âAI police.â Those police will need a way of interpreting the legality of an AI agentâs {âmentalâ state; actions} and mapping them only existing laws.
But if you need to do that for effective enforcement, I donât see why (from a societal perspective) we shouldnât just do that on the actorâs side and not the âpoliceâsâ side. Baking the enforcement into the agents has the benefits of:
Not incentivizing an arms race
Giving the enforcerâs a clearer picture of the AIâs âmental stateâ
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 AIâs actions, then that automatically disincentivizes AI systems from breaking the law.
(Iâm 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 doesnât seem necessary in the world where weâve solved alignment.)
I donât see why (from a societal perspective) we shouldnât just do that on the actorâs side and not the âpoliceâsâ side.
I agree that doing it on the actorâs 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 isnât bound by law.
E.g. if you have a chauffeur who refuses to exceed the speed limit (in a country where the speed limit thatâs actually enforced is 10mph higher), you fire that chauffeur and find a different one.
(Also, Iâm assuming youâre 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, Iâd make the second argument that it isnât 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 âthe 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 wonât take itâ.
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 theyâre 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 theyâre used?
Iâm 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 doesnât 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).
Couldnât the AI end up misaligned with the owners by accident, even if theyâre 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)+ââi=1IMi(x)
where f is bounded to have a range of length â€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 canât 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.
Couldnât the AI end up misaligned with the owners by accident, even if theyâre aligned with the rest of humanity?
Yes, but as I said earlier, Iâm assuming the alignment problem has already been solved when talking about enforcement. I am not proposing enforcement as a solution to alignment.
If you havenât solved the alignment problem, enforcement doesnât help much, because you canât 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.)
This is valuable information. However, some ML people I have talked about this with have given positive feedback, so I think you might be overestimating the difficulty.
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.
This seems to require the same type of fundamental ML research that I am proposing: mapping AI actions onto laws.
Certainly you still need legal accountabilityâwhy wouldnât we have that? If we solve alignment, then we can just have the AIâs 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 âthe 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 wonât take itâ.
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 âprogramming AI to follow lawâ 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 Iâd 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 agentâs 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 youâve said, for 2. to be most effective, you probably need âAI police.â Those police will need a way of interpreting the legality of an AI agentâs {âmentalâ state; actions} and mapping them only existing laws.
But if you need to do that for effective enforcement, I donât see why (from a societal perspective) we shouldnât just do that on the actorâs side and not the âpoliceâsâ side. Baking the enforcement into the agents has the benefits of:
Not incentivizing an arms race
Giving the enforcerâs a clearer picture of the AIâs âmental stateâ
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 AIâs actions, then that automatically disincentivizes AI systems from breaking the law.
(Iâm 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 doesnât seem necessary in the world where weâve solved alignment.)
I agree that doing it on the actorâs 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 isnât bound by law.
E.g. if you have a chauffeur who refuses to exceed the speed limit (in a country where the speed limit thatâs actually enforced is 10mph higher), you fire that chauffeur and find a different one.
(Also, Iâm assuming youâre 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, Iâd make the second argument that it isnât 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 theyâre 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.
Iâm 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 doesnât seem like a strong argument to focus on case 1.
Couldnât the AI end up misaligned with the owners by accident, even if theyâre 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 â€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 canât 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, Iâm assuming the alignment problem has already been solved when talking about enforcement. I am not proposing enforcement as a solution to alignment.
If you havenât solved the alignment problem, enforcement doesnât help much, because you canât 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.)