When one uses mathematics to clarify many AI alignment solutions, or even just to clarify Monte Carlo tree search as a decision making process, then the mathematical structures one finds can often best be interpreted as being mathematical counterfactuals, in the Pearl causal model sense. This explains the interest into counterfactual machine reasoning among many technical alignment researchers.
To explain this without using mathematics: say that we want to command a very powerful AGI agent to go about its duties while acting as if it cannot successfully bribe or threaten any human being. To find the best policy which respects this ‘while acting as if’ part of the command, the AGI will have to use counterfactual machine reasoning.
Why is counterfactual reasoning a matter of concern for AI alignment?
When one uses mathematics to clarify many AI alignment solutions, or even just to clarify Monte Carlo tree search as a decision making process, then the mathematical structures one finds can often best be interpreted as being mathematical counterfactuals, in the Pearl causal model sense. This explains the interest into counterfactual machine reasoning among many technical alignment researchers.
To explain this without using mathematics: say that we want to command a very powerful AGI agent to go about its duties while acting as if it cannot successfully bribe or threaten any human being. To find the best policy which respects this ‘while acting as if’ part of the command, the AGI will have to use counterfactual machine reasoning.