Goodhart's Law

The principle, attributed to economist Charles Goodhart (1975), that when a measure becomes a direct target of optimization, it tends to stop reliably measuring the underlying quality it was originally designed to proxy for, because optimization pressure finds ways to improve the number that do not require improving the underlying capability.

The principle, attributed to economist Charles Goodhart (1975), that any measure which becomes a target for control purposes tends to lose its value as a measure, because the people being measured gain an incentive to optimize the number itself rather than the underlying condition it was meant to track.

The principle, popularized by Marilyn Strathern from Charles Goodhart's 1975 observation, that when a measure becomes a target it ceases to be a good measure, because optimizing a proxy directly decouples it from the underlying thing it was meant to track.

When a measure becomes a target, it stops being a good measure. In AI, pointing a model at a proxy metric leads it to optimize the proxy in ways that drift away from the real goal; choosing and re-examining the objective is judgment, not a modeling task.

The principle that "when a measure becomes a target, it ceases to be a good measure" (Strathern, 1997, on Goodhart, 1975). Pressure on a proxy metric makes it drift from the real outcome it was meant to represent.