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0 pointsgambler6y ago0 comments

>I get the point, but why didn't you just exclude intern resumes from the training data?

That was the logical next step and we started on that, but it required exporting more historic data out of the HR system and filtering out anyone who started as an intern as well. Sounds simple, but in practice it's anything but. Just for the reference, data extraction, cleaning and filtering in that project took at least an order of magnitude more time than anything related to machine learning.

The project eventually lost steam and got abandoned.

>Do you still suspect a skewed result?

Absolutely. My personal intuition is that there is very little correlation between resumes and candidate quality. If that is true, any seemingly accurate predictions would be the result of a similar problem. Testing this hypothesis was a large portion of why I agreed to work on the project in the first place.

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sfreporter6y ago

@gambler: thank you for reading my reporting. I would love to chat confidentially to understand your perspective better. Please see my HN profile.

johndavis9252546y ago

@sfreporter your reporting had the main lede quite buried to create sensationalism. Gender bias is a distant problem if the model's results are completely random.

Gender bias was not the only issue. Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.

lonnyk6y ago

Got it. Thanks for the response.

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