4 ms·
Maybe, but it seems more likely that the model just didn't work. From the article: "Gender bias was not the only issue. Problems with the data that underpinned
by KevinEldon 8y ago
Maybe, but it seems more likely that the model just didn't work.
From the article: "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... With the technology returning results almost at random"
- WalterSear 8y agoSounds like they've successfully emulated their real world interview process.
- sanxiyn 8y agoIndeed. I am quite curious about details of the model. For example, the single largest contribution to real world interview process variability is interviewer (for resume screening, who screened that resume, etc.). Wouldn't it be possible to code interviewer as categorical variable and separate resume-intrinsic? effect and interviewer effect? They must have tried this, haven't they?
- throwawaymath 8y agoThose aren't mutually exclusive. Technically speaking, the model can return results "almost at random" and still demonstrate a bias against any particular attribute if that bias is evident in the underlying training dataset. If there are strictly fewer women in the underlying training set, the model can still return something resembling a uniform distribution of candidates while exacerbating the diminished representation of women. To give a concrete example: you have a bag of blue dice and red dice. There is a supermajority of blue dice in the bag. Your algorithm selects a single die out of the bag on every iteration. The output sequence of dice numbers appears uniform, but there are more blue dice than red dice in the output sequence.