Thanks for another thoughtful response and explaining further. I think we can now both agree that we disagree (at least in certain respects) ;-)
We take seriously your argument that AI could get really smart and good at predicting human preferences and values, which could change the level of human involvement in training, evaluation, and monitoring. However, if we go with the approach you propose:
> Instead, I think our strategy should be “If humans are inconsistent and disagree, let’s strive to learn a notion of human values that’s robust to our inconsistency and disagreement.”
> A committee of humans reviewing an AI’s proposal is, ultimately, a physical system that can be predicted. If you have an AI that’s good at predicting physical systems, then before it makes an important decision it can just predict this Committee(time, proposal) system and treat the predicted output as feedback on its proposal. If the prediction is accurate, then actual humans meeting in committee is unnecessary.
The question arises:
How will we know if AI has learned a notion of human values that’s robust to inconsistency and disagreement and that its predictions are accurate?
We would argue some form of human input would be needed to evaluate what the AI has learned. Though this input need not be prompt-response feedback typical of current RLHF approaches.
If this evaluation reveals that the AI is indeed accurate (whatever that may mean for the particular product and context in question), then we agree that further human input could be more limited. Though continual training, evaluation, and monitoring with humans in the loop in some capacity will likely be needed since values change over time and to ensure that the system has not drifted.
> (And indeed, putting human control of the AI in the physical world actually exposes it to more manipulation than if the control is safely ensconced in the logical structure of the AI’s decision-making.)
We are hesitant to take an approach of AI paternalism where we assume the AI knows best and ignore human disagreement, though there may be deployment contexts where that is appropriate for safety. Though note that our argument is focused more on human involvement in training, evaluation, and monitoring than real-time decisions during deployment. As AI gets smarter, even if these systems can perfectly predict human values and preferences, they could also learn to collude, deceive, and sabotage. For example, if they develop situational awareness, they could behave differently at deployment time than at training time. We agree that there are risks to enabling human control, but abdicating all control to the AI is also risky. This is why we argue for human-AI complementarity – leveraging the strengths of both types of intelligence may lead to a more robust signal for training, evaluation, and monitoring than relying on AI or humans alone.
Thanks for another thoughtful response and explaining further. I think we can now both agree that we disagree (at least in certain respects) ;-)
We take seriously your argument that AI could get really smart and good at predicting human preferences and values, which could change the level of human involvement in training, evaluation, and monitoring. However, if we go with the approach you propose:
> Instead, I think our strategy should be “If humans are inconsistent and disagree, let’s strive to learn a notion of human values that’s robust to our inconsistency and disagreement.”
> A committee of humans reviewing an AI’s proposal is, ultimately, a physical system that can be predicted. If you have an AI that’s good at predicting physical systems, then before it makes an important decision it can just predict this Committee(time, proposal) system and treat the predicted output as feedback on its proposal. If the prediction is accurate, then actual humans meeting in committee is unnecessary.
The question arises:
How will we know if AI has learned a notion of human values that’s robust to inconsistency and disagreement and that its predictions are accurate?
We would argue some form of human input would be needed to evaluate what the AI has learned. Though this input need not be prompt-response feedback typical of current RLHF approaches.
If this evaluation reveals that the AI is indeed accurate (whatever that may mean for the particular product and context in question), then we agree that further human input could be more limited. Though continual training, evaluation, and monitoring with humans in the loop in some capacity will likely be needed since values change over time and to ensure that the system has not drifted.
> (And indeed, putting human control of the AI in the physical world actually exposes it to more manipulation than if the control is safely ensconced in the logical structure of the AI’s decision-making.)
We are hesitant to take an approach of AI paternalism where we assume the AI knows best and ignore human disagreement, though there may be deployment contexts where that is appropriate for safety. Though note that our argument is focused more on human involvement in training, evaluation, and monitoring than real-time decisions during deployment. As AI gets smarter, even if these systems can perfectly predict human values and preferences, they could also learn to collude, deceive, and sabotage. For example, if they develop situational awareness, they could behave differently at deployment time than at training time. We agree that there are risks to enabling human control, but abdicating all control to the AI is also risky. This is why we argue for human-AI complementarity – leveraging the strengths of both types of intelligence may lead to a more robust signal for training, evaluation, and monitoring than relying on AI or humans alone.
~ Sophie Bridgers (on behalf of the authors)