Implicit consequentialism
Consider a machine that does exactly what its user would tell it to do. If the user is a consequentialist, then so is the machine.
But building this machine does not introduce any new goals into the world at all. All of its consequentialism flows through the user’s head — it merely amplifies the goal-directed reasoning that already happens there. There is no room to err in specifying its goals, because its goals are not specified.
This is the best case for act-based approaches to AI control.
But: this system may be optimizing internally, and is itself optimized.
We aim for all of this optimization to be a reflection and amplification of the user’s preferences.
But: the user’s reasoning is not perfect, and they may want AI to go beyond their capabilities.
We aim for humans to collaborate effectively with AI systems, forming teams that share human preferences and whose foresight exceeds the individual systems they are overseeing.
This project doesn’t seem easy, but I feel optimistic.
Parents:
- Paul Christiano's AI control blog
Speculations on the design of safe, efficient AI systems.
Darn it, I wanted to use this term to distinguish “not-explictly-consequentialistically optimizing for \(Y\) still optimizes for \(X\) when \(X\) is being varied and is causally relevant to \(Y\)” from “having an explicit model of \(X\) being relevant to \(Y\) and therefore explicitly forming goals about \(X\) and searching for strategies that affect \(X.\)” (E.g., natural selection does implicit consequentialism, humans do explicit consequentialism.) I’m not sure if I can think of an equally good replacement term for the thing I wanted to say. Would “proxy consequentialism” work for the thing you wanted to say?