Goodhart’s Law states that when a proxy for some value becomes the target of optimization pressure, the proxy will cease to be a good proxy. One form of Goodhart is demonstrated by the Soviet story of a factory graded on how many shoes they produced (a good proxy for productivity) – they soon began producing a higher number of tiny shoes. Useless, but the numbers look good.

Goodhart’s Law is of particular relevance to AI Alignment. Suppose you have something which is generally a good proxy for “the stuff that humans care about”, it would be dangerous to have a powerful AI optimize for the proxy, in accordance with Goodhart’s law, the proxy will breakdown.

Goodhart Taxonomy

In Goodhart Taxonomy, Scott Garrabrant identifies four kinds of Goodharting:

• Regressional Goodhart—When selecting for a proxy measure, you select not only for the true goal, but also for the difference between the proxy and the goal.

• Causal Goodhart—When there is a non-causal correlation between the proxy and the goal, intervening on the proxy may fail to intervene on the goal.

• Extremal Goodhart—Worlds in which the proxy takes an extreme value may be very different from the ordinary worlds in which the correlation between the proxy and the goal was observed.

• Adversarial Goodhart—When you optimize for a proxy, you provide an incentive for adversaries to correlate their goal with your proxy, thus destroying the correlation with your goal.

See Also

• Groupthink, Information cascade, Affective death spiral

• Adaptation executers, Superstimulus

• Signaling, Filtered evidence

• Cached thought

• Modesty argument, Egalitarianism

• Rationalization, Dark arts

• Epistemic hygiene

• Scoring rule