In the article I noticed: "Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner

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.)