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'In several cases, the programs failed to recognize the photos as featuring a human face at all.'

Last month, a research piece in MIT News was published confirming what some folks have suspected through current experiences with facial-recognition systems thus far: the systems were not reliably with darker-skinned people. Disappointing alas not surprising.

Study finds gender and skin-type bias in commercial artificial-intelligence systems

Effectively, the research found that there is gender and skin-type bias in commercial artificial-intelligence systems.

[...]And it's not just about computer vision. I'm really hopeful that this will spur more work into looking at [other] disparities.

One might ask the question, "What's the big deal?" Well, this can have (and already has had) adverse impact on individuals' lives, especially the already marginalized.

Among a number of questions, this research elevates the importance of why representation matters: it matters not only in big tech but all across society, e.g. elected positions, professional leadership positions, in the news, in the classroom, etc.

'To fail [...] on something that's been reduced to a binary classification task, you have to ask, would that have been permitted if those failure rates were in a different subgroup?' Buolamwini says.

"Refusal to acknowledge unconscious gender bias today is akin to denying the world is round," says MIT Prof. Emerita Nancy Hopkins. "Top talent is distributed among diverse groups. You can only be the best by being diverse." #IDWGIS

To help address the lack of representation across industries, we can start by removing barriers-of-entry for marginalized communities, starting with making higher education more accessible to everyone and valuing alternative forms of higher education, such as vocational and trades. When we consider everyone in our communities, restricting access to education serves to maintain the current power structure.

Equally important, existing policies and laws need to be reviewed regularly and rewritten with an inclusive, intersectional lens to address any and all forms of discrimination.