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An unethical optimization principle

Nicholas Beale, Heather Battey, Anthony C. Davison, Robert S. MacKay

If an artificial intelligence aims to maximize 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 pU of picking an unethical strategy can become large; indeed, unless returns are fat-tailed pU tends to unity as the strategy space becomes large. We define an unethical odds ratio, Υ (capital upsilon), that allows us to calculate pU from η, and we derive a simple formula for the limit of Υ as the strategy space becomes large. We discuss the estimation of  Υ and pU in finite cases and how to deal with infinite strategy spaces. We show how the principle can be used to help detect unethical strategies and to estimate η. Finally we sketch some policy implications of this work.

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