(Disclaimer: I am an Amazon employee sharing his own experience, but do not speak in any official capacity for Amazon. I don't know anything about the system me

(Disclaimer: I am an Amazon employee sharing his own experience, but do not speak in any official capacity for Amazon. I don't know anything about the system mentioned in this article.) I am a frequent interviewer for engineering roles at Amazon. As part of the interview training and other forums, we often discuss the importance of removing bias, looking out for unconscious bias, and so on. The recruiters I know at Amazon all take reaching out to historically under-represented groups seriously. I don't know anything about the system described in the article (even that we had such a system), but if it was introducing bias I'm glad it's being shelved. Hopefully this article doesn't discourage people from applying to work at Amazon - I've found it a good place to work. To say something about the AI/ML aspect of the article: I think as engineers our instinct is "Here's some data that's been classified for me, I can use ML/AI on it!" without thinking through all that follows, including doing quality assurance. I think a lot of focus in ML (at least in what I've read) has been on generating models, and not nearly enough focus has been on generating models that interpretable (i.e., give a reason along with a classification).