This piece, Most engineers are white — and so are the faces they use to train software – Recode, implies that AI software doesn’t do a good job recognizing non-white faces because most engineers (i.e. software developers) are white. I’d argue that the AI does a poor job because of this: the developers aren’t very good.
Good software developers, in particular the lead developers, take an active role in ensuring they have good test data. The success of their software when it goes live is dependent on it. Anyone using training data (i.e. using test data) in AI projects that is not using a broad set of faces is doing a poor job. Period. Regardless of whether or not they are white.
If the AI is supposed to do something (i.e. recognize all faces) and it does not, then the AI sucks. Don’t blame it on anything but technical abilities.
Because if you don’t have augmented intelligence, and if you solely depend on AI like software, you get problems like this, whereby automated software triggers an event that a trained human might have picked up on.
AI and ML (machine learning) can be highly probabilistic and limited to the information it is trained on. Having a human involved makes up for those limits. Just like AI can process much more information quicker than a limited human can.
See the link to the New York Times story to see what I mean.
If you want a better understanding of artificial intelligence or if you want to gain some insight into the future of machine learning, I recommend these two free reports, found here: Free AI Reports from O’Reilly Media. There’s so much hype and speculation about AI: these reports cut through all that noise and they will give you a better understanding of what A.I. really is and where it is going.
P.S. If you like them, check out the many great non-A.I. related reports as well. You don’t have to be a technologist to be able to read them.