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Comment by bcheung | Hacker News Reader

How did you control for these things? Wondering what patterns there are that people use to prevent social discrimination.

Seems challenging since much of AI, especially classification, is essentially a discrimination algorithm.

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There are a few ways you can tackle this issue: 1) have the same algorithm for each group, but train separately (so in the end you have two different weights); 2) over-sample the group under represented in the data; 3) make the penalty more severe for guessing wrongly on female then male applicants during training; 4) apply weights to gender encoding; 5) use more then just resumes as data.

This isn't an insurmountable problem, but does require extra work then just "encode, throw it in and see what happens".

Amazon only scrapped the original team, but formed a new one in which diversity is a goal for the output.

Or: don't include gender in the training data.

They didn’t. It was discovered through other signals (mention of membership in “women’s” clubs etc.

So they did. It should be obvious that if you don't want to include gender, then you have to sanitize gender-related data.

That's not as easy as one might think.

Machine learning generally doesn't have any prior opinions about things and will learn any possible correlation in the data.

It could for example discover that certain words or sentence structures used in the resume are more likely associated with bad candidates. Later you find out that <protected class> has a huge amount of people that use these certain words/structures while most other people don't.

And now the AI discriminates against them.

ML will pick up on any possible signal including noise.

More than that, though. Graduates of all-women colleges were also caught. If you're using school as a data point, that's extremely hard to sanitize.

Then what is the purpose of this? At some point you want this thing to "discriminate" (or "select", if this is a better word) people based on what they have done in life. Which is not negative per se.

But you don't want it to select based on gender.

Would it though? A school name is essentially just that, no gender information there, even with the "women" prefix. If you discriminate other schools, you can do it too with those. FWIW there could be a difference in performance which the ML finds.

It would. Just because it's not explicitly looking for a "W" in the gender field doesn't mean it's not able to determine gender and discriminate based on that. The article and the discussion is all about how these things, despite not explicitly being told to discriminate based on gender, or race, or any number of factors, can still end up gathering a set of factors that are more prevalent among those groups, and discriminate against those people all the same.

>despite not explicitly being told to discriminate based on gender, or race, or any number of factors

Then this is completeley useless. You want this "AI" to discriminate based on a number of things. That's the whole point. You want to find people that can work for you. If a specific school or title is a bad indicator (based on what you hired now), then it just is that.

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