Addressing model bias by adjusting which data the model has access to is a bad idea. Tweaking the data so that the model output looks equitable is going to make

Addressing model bias by adjusting which data the model has access to is a bad idea. Tweaking the data so that the model output looks equitable is going to make your model across the board. You should train your model on what you have and then add explicit biases to the classifier for different groups. That way you have the best model and are clear on your biases. If this model is equally accurate for black and white women that means that either race is not a factor in predictability, that it is a factor but easily adaptable into a model, or race is a factor and they’ve reduced their ability to diagnose one group in the name of equity. The linked article suggests accuracy gains are due to better risk models, that use more than age. I’m not sure if that means it’s tied into the image neural net. Would like to see false positive rate too.