Tagged: bias

The results from Joy Buolamwini's research on facial recognition accuracy are disappointing to say the least.

Microsoft’s error rate for darker-skinned women was 21 percent, while IBM’s and Megvii’s rates were nearly 35 percent. They all had error rates below 1 percent for light-skinned males.

Those are bad numbers, but they shouldn't be surprising—not when we're training these algorithm's with a poorly constructed data set.

One widely used facial-recognition data set was estimated to be more than 75 percent male and more than 80 percent white, according to another research study.

The stakes are just too high for us to continue to build technology without making sure we're taking off our blinders and accounting for our biases. Oversights like this leave people out, at best. At their worst, they are capable of doing even worse.

Even in my tiny design practice, every decision I make is shaped by my biases; every decision I make is capable of harm. And it’s so, so easy to forget this: to focus on the layout challenge in front of me, to fulfil the client’s latest request, or to meet a business goal. When I do these things, I occasionally forget to ask myself who’ll be impacted by my work and, most importantly, to ask how I can mitigate that harm.

A fantastic breakdown of the impact media has on how we perceive reality. The post starts by looking at the huge difference between coverage of terrorist attacks and the reality, demonstrating that media's fixation (and the attention we give those articles) makes terrorism seem far more prevalent than it is.

Then there's this sobering, accurate and important conclusion:

In addition to selective data, as readers we’re over-generalizing our view of the entire other side , based on extreme events or commentary from a select fringe.

Here’s the thing: the more we experience how other browsers work, the more we learn about how different users experience our websites.