We Must Fix AI’s Diversity Problem

A lack of diverse staff is creating racist, sexist and classist intelligence

Pepper, a white robot developed by Softbank Robotics. Photo by Alex Knight on Unsplash.

“Well, right now I’m focused on women, you know, and it’s back to Marc [Benioff]’s focus on priorities. I have employees, that are, you know, other types of diversity coming to me and saying well why aren’t we focused on these other areas as well, and I said yes we should focus on them but, you know, the phrase we use internally is ‘If everything is important, then nothing is important.’

This may be a lesson in prioritization for shipping software and meeting sales targets, but it is a single-minded approach that discriminates against the under-represented groups that need the most help.

Diversity issues deployed

Consider the following video from 2009. It shows how the face tracking functionality of a Hewlett Packard webcam seems to be unable to recognize black faces.

The full picture of bias

In her keynote talk at NIPS 2017, Kate Crawford, the co-director and co-founder of the AI Now Institute, explores how bias in the AI systems that we create today are propagating historical and present discriminatory behavior that we see in society itself. Given that AI systems are becoming prevalent in ways that truly affect the outcome of people’s lives, fixing bias is an important issue for the industry to address. Crawford defines bias as a skew that causes a type of harm. She classifies the effect of bias into two broad areas: harms of allocation and harms of representation.

Turkish is a gender-neutral language, so the translation back to English is subject to bias.

Fixing AI bias

So what do we do about bias in AI? Given that we can hypothesize that the impact of the industry’s innovation on the world will only ever increase, we need to make a stand now to prevent existing discrimination in our society spreading virally.

VP Engineering @brandwatch. Writing things that interest me. Hopefully they'll interest you as well.

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