zlacker

This article links at the bottom to similarly-styled piece about "mathwashing", the idea that it's morally wrong for an algorithm to notice true facts about reality. That idea is utter bunk, and so likely is "Social Cooling" as well. Both pushes are really about unelected activists trying to limit other people's technology to bring about their peculiar idea of Utopia.

In all human history, efforts to hold back the tide of technological progress have never worked. Instead of adopting a Luddite fear of data and math, we should use both for all useful ends as soon as possible.

>>quotem+(OP)
the problem isn't necessarily with _accurate_ data and math, but with reductive statistics that paint with broad brushes. Statistics inherently remove nuance, which is fine when the nuance is unimportant to what you're measuring, but not when it's actually important.

The example about cancer doctors in TFA is perfect. "more deaths = worse doctor" is a poor metric, because advanced cases have higher deaths in general, leading to a disincentive to try to help people with advanced forms of cancer. That's a terribly perverse incentive, and one that should be avoided.

Fundamentally, a lot of this stuff comes down to a lack of nuance in metrics, leading to some nasty effects down wind.

>>quotem+(OP)
Men commit more violent offenses than women. Should I, a man, be turned down for a job because of this despite never having committed a violent offense? No woman has ever won the US presidency, should we therefore divert funds away from female candidates because they are provably less likely to win? If after doing that for a century and no woman has yet to win, should we still continue to divert funds away from them? I hope the answers to these questions are obviously "no, we shouldn't." If data-driven decisions are equally problematic but hide it behind layers of apparently justifiable (and often opaque) mathematics, then we have a problem.

>>quotem+(OP)
You missed the point. The point is that you can be doing the wrong math on the wrong data, jumping to completely baseless and detrimental conclusions. You cannot judge the needs of the many by the actions of the few or vice versa.