[Agingcoder]

Thanks a lot for taking the time to reply. I have to admit that I have found your post unexpectedly super interesting. It's quite subtle, and while I can see all the facts, there are a few logical steps which don't quite make sense to me.

So you're saying that 1. Unlike what the blog post says, there is practice bias even in radiology. This would indeed explain why the model can learn 'racial bias'.

2. This is less clear to me. Just like humans can't see race on radio images, humans might be unable to see differences for diseases like pneumonia, but the nn could see them, no? In other words, how do you know that the differences have to come from hidden racial bias, and not from hidden pathological differences (that you don't know about, just like you've just discovered hidden racial bias) ?

[Agingcoder]

(replying to myself about 2) You say the race can't be seen in the image by humans because it's not here, and therefore the race information must come from bias introduced by the radiologist.

I guess it depends on whether all differences are visible to the human eye (and there's nothing pathological hidden that the nn 'sees') , and whether you can prove (a very hard thing in stats) that there's no way you can possibly extract race in a different manner.

Now, assuming the data is racially biased because of medical practice biased, forgive me for being naive, but, why is this surprising / major?

Isn't this just another instance of models being trained on bad data, and there are already plenty of examples in ia ?

Edit:.. Unless the actual finding becomes 'radiology is (unexpectedly) racially biased, so much so that an ia can learn it'?

[lukeor]

I never said radiology didn't produce biased results, just that we don't know the race of our patients most of the time.

There are lots of ways bias can still occur, like in who gets referred for scans, when they get referred, what the referrer writes on the request form, how the technologist takes the images (I could tell you some horribly racist stories about a few ultrasonographers I've worked with), and so on.

And all of this is based on previous work that AI produces bias (when trained on these datasets). If it was useful differences that drove AI learning about race, the models would not produce disparities. We went looking for how it is interacting with race because we already knew it was producing unacceptable outcomes. The big news here is that it is so easy to learn race that this effect is almost certainly not isolated to the systems tested so far.

[Agingcoder]

Thanks!