Too much efficiency makes everything worse (sohl-dickstein.github.io)

A recent analysis highlights the paradox that increased efficiency, particularly in standardized testing and machine learning, can lead to worse outcomes, a phenomenon coined the "strong version of Goodhart's law." This concept posits that as we optimize a proxy measure—like test scores for student performance or accuracy for machine learning models—the actual goals can deteriorate. For instance, schools may excessively focus on teaching to the test at the expense of fostering comprehensive skills, ultimately diminishing the educational quality. In the realm of machine learning, this relates to overfitting, where models improve on training data but fail to generalize to real-world scenarios, potentially causing performance to decline. This insight is significant for the AI/ML community as it emphasizes the risks of over-optimizing proxy objectives. The strong version of Goodhart's law suggests that excessive efficiency can harm the very metrics designed to gauge success. By recognizing this connection, researchers are encouraged to explore mitigations analogous to those used in machine learning, such as aligning proxy measures with desired outcomes, employing regularization to prevent complexity, and introducing randomness into optimization processes. This broader understanding can inform not just AI systems but also economic and social models, steering them toward genuinely beneficial outcomes rather than mere numerical improvements.