An Unethical Optimization Principle

If an artificial intelligence aims to maximise risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective function allows sufficiently for this risk. Even if the proportion $η$ of available unethical strategies is small, the probability ${p_U}$ of picking an unethical strategy can become large; indeed unless returns are fat-tailed ${p_U}$ tends to unity as the strategy space becomes large. We define an Unethical Odds Ratio Upsilon ($Υ$) that allows us to calculate ${p_U}$ from $η$, and we derive a simple formula for the limit of $Υ$ as the strategy space becomes large. We give an algorithm for estimating $Υ$ and ${p_U}$ in finite cases and discuss how to deal with infinite strategy spaces. We show how this principle can be used to help detect unethical strategies and to estimate $η$. Finally we sketch some policy implications of this work.