I recently learned about Goodhart's Law. Simply put,
When a measure becomes a target, it ceases to be a good measure.
However, in Data Science, we really do aim at improving our performance by increasing or decreasing a metric, and improve our models based on that. For instance, in Kaggle competitions. Is Goodhart's Law applicable to Data Science? Why or why not?