biases manifest in really strange and unexpected ways and you’ll fail even when you try to intentionally account for them; that’s why things like facial recognition success rate correlates to the darkness of your skin or why successful ai recognition of text/speech is related how different your language is to english or mandarin.
the only way to successfully gaurd against bias in software development is to have developer teams comprised of people can naturally keep each other’s biases in check.
I think facial recognition technology is very different to threw diverse software. The fact that those technologies are trained on predominantly-white data is no surprise, both of your examples are data-based (ML models) where the data itself contains the bias.
I am talking more of the open-source projects, it’s important; as you rightfully call out, that we have a varied group of opinions within the developer group 👍
biases manifest in really strange and unexpected ways and you’ll fail even when you try to intentionally account for them; that’s why things like facial recognition success rate correlates to the darkness of your skin or why successful ai recognition of text/speech is related how different your language is to english or mandarin.
the only way to successfully gaurd against bias in software development is to have developer teams comprised of people can naturally keep each other’s biases in check.
I think facial recognition technology is very different to threw diverse software. The fact that those technologies are trained on predominantly-white data is no surprise, both of your examples are data-based (ML models) where the data itself contains the bias.
I am talking more of the open-source projects, it’s important; as you rightfully call out, that we have a varied group of opinions within the developer group 👍