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Why would one train on "all hires CVs"? It'd be "engineering CVs", moreover it'd be "engineering applicants CVs", not "engineering hires CVs".

michaelt 8y ago
Because you were trying to build a system that would perform CV filtering for your entire company, and you figured Deep Learning would just kinda take care of everything. You're right you'd want to look at applicants' CVs - I skipped over that to make the numbers readily comprehensible.
Bartweiss 8y ago
In the article I noticed: "Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs" That language is a bit ambiguous, it could just mean that the algorithm failed on a wide variety of jobs beyond engineering. But another reading suggests that the algorithm was not asked "is this person a good fit for this role" but instead "what, if anything, is this person qualified for?" If that's the case, then the problem starts to make more sense: the algorithm learned a correlation between male-sounding resumes and being hired for engineering roles. That could produce a biased approach even if the decisions in the training data were gender neutral but position-specific. Of course, it would also mean that an Amazon ML team trained an algorithm with inputs that didn't match to its eventual task, and makes me wonder what they used as a test set... (Anecdotally, Amazon spent quite a while recruiting me for SysEng work I'm wildly unqualified for and uninterested in, even suggesting a switch to applying for that team when I was already in the funnel for something I'm more qualified at. When my resume eventually made it to a syseng engineer, they were rightly baffled that I had landed on in their stack, giving me the sense that something was screwy with how Amazon decides who heads towards which role.)
megy 8y ago
Because we are guessing at the problem, we don't have complete data.

michaelt 8y ago

Because you were trying to build a system that would perform CV filtering for your entire company, and you figured Deep Learning would just kinda take care of everything. You're right you'd want to look at applicants' CVs - I skipped over that to make the numbers readily comprehensible.

Bartweiss 8y ago

In the article I noticed: "Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs" That language is a bit ambiguous, it could just mean that the algorithm failed on a wide variety of jobs beyond engineering. But another reading suggests that the algorithm was not asked "is this person a good fit for this role" but instead "what, if anything, is this person qualified for?" If that's the case, then the problem starts to make more sense: the algorithm learned a correlation between male-sounding resumes and being hired for engineering roles. That could produce a biased approach even if the decisions in the training data were gender neutral but position-specific. Of course, it would also mean that an Amazon ML team trained an algorithm with inputs that didn't match to its eventual task, and makes me wonder what they used as a test set... (Anecdotally, Amazon spent quite a while recruiting me for SysEng work I'm wildly unqualified for and uninterested in, even suggesting a switch to applying for that team when I was already in the funnel for something I'm more qualified at. When my resume eventually made it to a syseng engineer, they were rightly baffled that I had landed on in their stack, giving me the sense that something was screwy with how Amazon decides who heads towards which role.)

megy 8y ago

Because we are guessing at the problem, we don't have complete data.