Facial recognition algorithms have been shown to be less accurate for black faces than for white ones. But why do facial recognition algorithms make more mistakes for Blacks than whites, and what can be done about it?
To err is human… and algorithmic
Like any prediction algorithm, facial recognition algorithms make probabilistic predictions based on incomplete data – a blurry photo, for example. Such predictions are never completely error-free, nor can they be. Since errors always exist, the question is what is an acceptable level of errors, what kind of errors should be prioritised, and whether you need a strictly identical error rate for every population group.
