Insurance companies charge men more than women for car insurance because the data shows they're more risky. Is that gender discrimination? There's a line somewhere between -isms and data driven decisions.
Is it gender discrimination: Yes. Is it legally protected gender discrimination: Also yes!
Welcome to systemic problems!
"Data Driven Decisions" are almost always about "this consumer/user group will probably do X". This is discriminatory against people in the group who don't/won't do X. Insurance companies (and college admissions boards, and police deciding where to send officers) do their best to try and remove broad categories (race, sex, etc) and tailor their data to a specific outcome, but that doesn't mean data can't be used to discriminate. "Lies, Damn Lies, and Statistics" after all.
There is a line, but it moves, and it's blurry, and sometimes it's not really a line but more of a circle. The point is, sometimes these things are bad/discriminatory but they're allowed to keep happening because that's how we've always done things or something to that effect.
Dealing with people as groups is at the heart of -isms and stereotyping/discrimination. It's also how data/statistics tend to treat people.
Welcome to systemic problems!
"Data Driven Decisions" are almost always about "this consumer/user group will probably do X". This is discriminatory against people in the group who don't/won't do X. Insurance companies (and college admissions boards, and police deciding where to send officers) do their best to try and remove broad categories (race, sex, etc) and tailor their data to a specific outcome, but that doesn't mean data can't be used to discriminate. "Lies, Damn Lies, and Statistics" after all.
There is a line, but it moves, and it's blurry, and sometimes it's not really a line but more of a circle. The point is, sometimes these things are bad/discriminatory but they're allowed to keep happening because that's how we've always done things or something to that effect.
Dealing with people as groups is at the heart of -isms and stereotyping/discrimination. It's also how data/statistics tend to treat people.