``` The researchers also found that they could retrain the models in a way that improves their fairness. However, their approach to "debiasing" worked best when

``` The researchers also found that they could retrain the models in a way that improves their fairness. However, their approach to "debiasing" worked best when the models were tested on the same types of patients on whom they were trained, such as patients from the same hospital. When these models were applied to patients from different hospitals, the fairness gaps reappeared. "I think the main takeaways are, first, you should thoroughly evaluate any external models on your own data because any fairness guarantees that model developers provide on their training data may not transfer to your population. Second, whenever sufficient data is available, you should train models on your own data," says Haoran Zhang, an MIT graduate student and one of the lead authors of the new paper. ``` This is just overfitting. Why are they training whole models on only one hospital worth of data when they appear to have access to five? They should be training on all of the data in the world they can get their hands on then maybe fine tuning on their specific hospital (maybe they have higher quality outcomes data that verifies the readings) if there are still accuracy issues. The last five years have taught us that gobbling up everything (even if it's not the best quality) is the way.