Submitted via IRC for chromas
Amazon scraps secret AI recruiting tool that showed bias against women
SAN FRANCISCO (Reuters) - Amazon.com Inc’s (AMZN.O) machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.
The team had been building computer programs since 2014 to review job applicants’ resumes with the aim of mechanizing the search for top talent, five people familiar with the effort told Reuters.
Automation has been key to Amazon’s e-commerce dominance, be it inside warehouses or driving pricing decisions. The company’s experimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars - much like shoppers rate products on Amazon, some of the people said.
[...] But by 2015, the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way.
That is because Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry.
In effect, Amazon’s system taught itself that male candidates were preferable. It penalized resumes that included the word “women’s,” as in “women’s chess club captain.” And it downgraded graduates of two all-women’s colleges, according to people familiar with the matter. They did not specify the names of the schools.
Amazon edited the programs to make them neutral to these particular terms. But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people said.
The Seattle company ultimately disbanded the team by the start of last year because executives lost hope for the project, according to the people, who spoke on condition of anonymity. Amazon’s recruiters looked at the recommendations generated by the tool when searching for new hires, but never relied solely on those rankings, they said.
rinciples.
(Score: 2, Insightful) by Anonymous Coward on Thursday October 11 2018, @08:46AM (7 children)
Correct, "biased against women" here is progressive code for "biased in favor of the better candidate". I suspect the womxn naturally inclined towards STEM fields always did well before forced "diversity" pulled the statistical average down for them.
(Score: 1) by khallow on Thursday October 11 2018, @12:05PM (2 children)
When would that have been? Some colleges didn't even start graduating women from technical fields until quite late. My mother was one of the first two female chemical engineers to graduate from her school in the late 60s. She got a lot of crap from professors and fellow students for her gender, mostly because they didn't think she could do it.
(Score: 1, Informative) by Anonymous Coward on Thursday October 11 2018, @01:16PM (1 child)
No [wikipedia.org] idea [wikipedia.org]
(Score: 2) by urza9814 on Friday October 12 2018, @03:01PM
So over an entire hundred years of world history and all of the various fields of endeavor that qualify as "STEM", you managed to find a whole two women who managed to succeed quite well. Congratulations, you've demonstrated that outliers exist; but I'm not sure how that's relevant to the discussion at hand.
(Score: 3, Touché) by DeathMonkey on Thursday October 11 2018, @05:41PM (3 children)
Correct, "biased against women" here is progressive code for "biased in favor of the better candidate".
So you assume, based on no evidence, that the woman is the worse candidate. No bias there!
(Score: 0) by Anonymous Coward on Thursday October 11 2018, @06:02PM (2 children)
No, I assume based on diversity quota hiring and it's a safe assumption.
(Score: 2) by DeathMonkey on Thursday October 11 2018, @06:24PM (1 child)
I think the current strategy is to at least pretend that you're not prejudiced.
Thanks for the honesty, I guess.
(Score: 0) by Anonymous Coward on Thursday October 11 2018, @09:01PM
We don't know how this "AI" was trained but the question remains, how could a statistical model that doesn't factor gender be biased on the basis of gender? There was a similar story out of (I think Australia) earlier this year where they removed gender from applications before consideration and found that was somehow biased against women too. I've worked with women, I've worked for women and the women I respected most didn't want to work with other women either. So no, there's no "prejudice", only experience.