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Yes but only in very poorly designed systems. We have those now with AI it's no different. I still see no reason to jump from 2 to 3 other than incompetence in
by reroute1 7y ago
Yes but only in very poorly designed systems. We have those now with AI it's no different. I still see no reason to jump from 2 to 3 other than incompetence in design.
- loopz 7y agoThe other poster stated "predictive model". A predictive model could easily identify males to be "more fit" (higher p-value) than females, in aggregate. The programmer tries to evade this bias, by ie. removing gender values in the system. However, the system STILL identify males "more fit", due to correllated values (likes Mancester United/Liverpool). Machine Learning is not much more than advanced statistics, so data biases gets amplified, thus naive application is often a poor fit. Even worse, the experts are at loss on how to make such predictive systems "perform better", without introducing biases on their own, since the data and categorizations themselves may be full of biases. In the real world, incompetence may not be a huge hurdle when selling complex systems. Also, these biases are invisible, until one thinks about them or spots them in the wild.
- crooked-v 7y agoLots of people think "AI" is a magic bullet that will obviate any need to actually think about this stuff.