And yet, they do.
Source: worked there, on a ML focused team.
Source: worked there, on a ML focused team.
It covers de-risking data if it isn't required for ML models or is of low impact. Through removal of it, masking of it, or entropy injection to it. Every single one of these approaches is detrimental to an ML model and just make it useless unless the fields being controlled are't correlated to the outcome in the first place.
This article is an acknowledgement of the issue and the fact that solutions don't exist. Only risk management strategies which disable potential solutions.
Their ultimate answer is 'protect the data and give it to only a small restricted set of people'. Right back where we started.
This behaviour is counterintuitive, but effectively you're enforcing a degree of regularization in a way similar to how data augmentation works. You'll get reduced training set performance and improved test set performance, as with other forms of regularization until you make it too strong.