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Earlier quoted context omitted.
Why not "women are just not as interested in math, logic and computer science to pursue it AS OFTEN as men"? Why are you not considering this possibility?
Because that's an inconvenient truth that doesn't fit the oppressor vs oppressed narrative.
I wish we could move away from resumes for tech role screening anyway, since they convey very little real reliable information. I’ve seen too many great hires from candidates with relatively weak resumes, and failed interviews from candidates with great resumes (and obviously vice versa). I’m not sure what the best alternative should be, though. I am a fan of open source work as a sort of code portfolio, but it doesn…
Unfortunately using open source work, even if only for programming, introduces all sorts of biases as well. A lot of very competent programmers work at jobs that do not have open source contributions and also have families which limit the time they can spend coding after work.
Technical talent is both expensive and a rare commodity for tech companies. The non-male engineers I've worked with have always been exceedingly competent, smart, and their differing perspectives invaluable. If there was an untapped market of engineers you'd better believe every tech company would be taking advantage of it.
This is the most interesting part to me. It’s suggesting that men and women think about things differently. Which, if true, suggests a lot of other possibilities that have relevance when hiring for certain positions.
I would guess that the training data for the ML set was the set of all resumes and an indicator of whether the candidate was eventually hired (maybe with supplemental data about how far in the process the candidate got). Could this be a direct indicator of a powerful subconscious bias in Amazon's existing hiring process?
At the least, they should interview a certain subset of randomly chosen applicants or else the feedback loop from the interviewing process and the AI is going to grow tighter and tighter.
I don't think we're anywhere close to general AI so any AI system out there is built on what we feed it. If your music suggestion algorithm has a biased AI that's one thing, but when you're using AI to make critical decisions in society like who gets hired/recurited, medical diagnoses and other things you need to be extremely careful.
When designing AI models the broadest set of viewpoints should be considered. Any piece of AI is simply a reflection of it's creators, we need to make sure that some sort of equitable consensus is reached before deploying AI to critical human scale issues.
What would happen if you removed gender from all HR and recruiting systems and then retrain the AI? Or for that matter, remove ethnicity, age, creed, etc... Is there any reason we need to be more specific? Race: Human Gender: Yes Age of legal contractual consent: Yes
The AI did not have access to gender. It was just word weighting and it turned out that words that could be linked to females ended up with applicants that had a negative outcome. Like the article says the AI ended up giving a negative weight to any resume containing the word women's as in women's [---] club , or those that mentioned certain all women's colleges.
The project was doomed from the start.
Earlier quoted context omitted.
Why not "women are just not as interested in math, logic and computer science to pursue it AS OFTEN as men"? Why are you not considering this possibility?
Ah, the Damore argument. Besides the fact that his psuedo science has been summarily handled[0], to consider his argument you then have to equally consider the possibility of sexism in academia pressuring women to not study these subjects and societal pressure their whole lives pressuring them to not persue these career paths. There's also the idea that lack of women scientist "heroes" can be limiting (lack of role m…
Helping individuals to overcome biology is much simpler than doing it at population scale.