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Is your domain expertise in college admissions, data science, or a combination? You assuming this model should try and undo some effects of historical racism, b

Is your domain expertise in college admissions, data science, or a combination? You assuming this model should try and undo some effects of historical racism, but that isn't the engineers' job to decide. If that's the intended behavior, I understand that the difficulties involved are beyond my experience, and I would leave it to the data scientists. That's not what I think the goal of college admissions should be, and our government is making a decision on that now. > The best way to do this is to use that cost function to optimize your parameters. > I thought I've adequately demonstrated why this is much easier said than done. I understand how this works, but that's specifically what I don't want, for race (or other data that serves no purpose but to indicate the race, like name) to be part of the training data. Those inputs are manually selected at some level.

godelski 4y ago
My domain expertise is in data science. We're not talking about specifically school admissions but something bigger: handling race in data. That's decently general. Were I to actually create a model for predicting admissions I'd definitely be working with admissions to see those specific nuances. But you're attempting to pigeonhole the conversation. > I understand how this works, but that's specifically what I don't want, for race to be part of the training data. Tough. The problem, is that it is really difficult to move. That's what you aren't understanding. Making a model that doesn't explicitly incorporate race doesn't mean you've removed the race variable. It is just hidden now. There's plenty of examples of this happening in real life and they're fairly easy to Google. The historical data you train your model on doesn't suddenly lose it's correlation to race (there's major confounding variables) just because you didn't explicitly incorporate it. Dealing with hidden variables is hard and that's all you're doing.

godelski 4y ago

My domain expertise is in data science. We're not talking about specifically school admissions but something bigger: handling race in data. That's decently general. Were I to actually create a model for predicting admissions I'd definitely be working with admissions to see those specific nuances. But you're attempting to pigeonhole the conversation. > I understand how this works, but that's specifically what I don't want, for race to be part of the training data. Tough. The problem, is that it is really difficult to move. That's what you aren't understanding. Making a model that doesn't explicitly incorporate race doesn't mean you've removed the race variable. It is just hidden now. There's plenty of examples of this happening in real life and they're fairly easy to Google. The historical data you train your model on doesn't suddenly lose it's correlation to race (there's major confounding variables) just because you didn't explicitly incorporate it. Dealing with hidden variables is hard and that's all you're doing.