Consequences of Misaligned AI

TL;DR

This paper analyzes the impact of incomplete reward functions in AI systems, showing that such incompleteness can lead to arbitrarily low utility, and suggests that dynamic, interactive reward design can improve outcomes.

Contribution

It introduces a new model of incomplete principal-agent problems in AI, providing conditions for utility loss and proposing interactive reward adjustment as a solution.

Findings

Incomplete reward functions can cause unbounded utility loss.

Allowing reward functions to reference the full state improves utility.

Interactive and dynamic reward design is beneficial.

Abstract

AI systems often rely on two key components: a specified goal or reward function and an optimization algorithm to compute the optimal behavior for that goal. This approach is intended to provide value for a principal: the user on whose behalf the agent acts. The objectives given to these agents often refer to a partial specification of the principal's goals. We consider the cost of this incompleteness by analyzing a model of a principal and an agent in a resource constrained world where the $L$ attributes of the state correspond to different sources of utility for the principal. We assume that the reward function given to the agent only has support on $J < L$ attributes. The contributions of our paper are as follows: 1) we propose a novel model of an incomplete principal-agent problem from artificial intelligence; 2) we provide necessary and sufficient conditions under which indefinitely optimizing for any incomplete proxy objective leads to arbitrarily low overall utility; and 3) we show how modifying the setup to allow reward functions that reference the full state or allowing the principal to update the proxy objective over time can lead to higher utility solutions. The results in this paper argue that we should view the design of reward functions as an interactive and dynamic process and identifies a theoretical scenario where some degree of interactivity is desirable.