A putative new idea for AI control; index here.

A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.

Stuart Russell

Think of an AI directing a car, given the instructions to get someone to the airport as fast as possible (optimised variables include "negative of time taken to airport") with some key variables left out - such as a maximum speed, maximum acceleration, respect for traffic rules, and survival of the passengers and other humans.

Call these other variables "unstated objectives" (UO), as contrasted with the "stated objectives" (SO) such as the time to the airport. In the normal environments in which we operate and design our AIs, the UOs are either correlated with the SOs (consider the SO "their heart is beating" and the UO "they're alive and healthy") or don't change much at all (the car-directing AI could have been trained on many examples of driving-to-the-airport, none of which included the driver killing their passengers).

Typically, SOs are easy to define, and the UOs are the more important objectives, left undefined either because they are complex, or because they didn't occur to us in this context (just as we don't often say "driver, get me to the airport as fast a possible, but alive and not permanently harmed, if you please. Also, please obey the following regulations and restrictions: 1.a.i.α: Non-destruction of the Earth....").

The control problem, in a nutshell, is that optimising SOs will typically set other variables to extreme values, including the UOs. The more extreme the optimisation, and the furthest from the typical environment, the more likely this is to happen.