Skip to navigationSkip to content
Close

AI systems are mostly built by rich white men. As we increasingly use them to do more consequential things, from screening job candidates to driving cars, negative societal effects can be immense. While engineers might double down on automation to tackle AI bias, it’s only humans who have a real chance of solving it.

Read more on Quartz

Featured contributions

  • Couldn't agree more - there are several steps that companies should (and must) take to mitigate data and algorithmic bias as they're collecting and annotating data, and during training and validation of AI models. But having a diverse team is equally important. Diverse teams can recognize and flag if

    Couldn't agree more - there are several steps that companies should (and must) take to mitigate data and algorithmic bias as they're collecting and annotating data, and during training and validation of AI models. But having a diverse team is equally important. Diverse teams can recognize and flag if certain demographics or use cases are underrepresented in datasets, even if done unintentionally. My company Affectiva faced that in our early days. Our data labeling team in Cairo flagged that we – at the time – did not have any data of women wearing a hijab, which was a huge oversight. So we set out to add that to our dataset. Moreover, diverse teams have the potential to think of new use cases for technology that are representative of different groups, and to solve challenges for different groups of people. Not only is this the right thing to do, but it’s good for business, and key for moving the industry forward.

  • The standard job seeking advice out there is essentially telling you to spend hours laboriously crafting a resume/cover letter tailored for each role - in order to get through an ATS gatekeeper, then a recruiter who won’t spend more than six seconds reading it - all in the hopes of fortuitously getting

    The standard job seeking advice out there is essentially telling you to spend hours laboriously crafting a resume/cover letter tailored for each role - in order to get through an ATS gatekeeper, then a recruiter who won’t spend more than six seconds reading it - all in the hopes of fortuitously getting to a hiring manager that isn’t predisposed to rejecting you due to some factor you’ll never know. Considering how important human relationships continue to be in recruiting, for job seekers it seems that algorithms only result in a colossal waste of time.