> The term "bias" leads you to believe the algorithms are generating results unrepresentative of wider reality, which simply isn't the case. You can make a str

> The term "bias" leads you to believe the algorithms are generating results unrepresentative of wider reality, which simply isn't the case. You can make a strong argument that, for example, the FB news feed algorithm leads to results that are unrepresentative of reality, because its input data (user clicks and likes) do not sufficiently represent reality accurately. Or from the article: "Nikon’s confusion about Asian faces and HP’s skin tone issues in their face recognition software both seem to be the product of learning from skewed example sets." Nowhere here is the algorithm being blamed, but we have an example of results that at least some people find objectionable due to patterns in the input data. Just to take a particular example, the algorithm's output has marked someone as blinking when they aren't, they're just Asian. That's a particular example of results being unrepresentative of reality. As much as you want to talk about the algorithms in isolation, using ML also includes figuring out what input data to use and also how to properly interpret the output.