Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.
Covid-19 expert Akiko Iwasaki fights a different virus: sexism in science - 0 views
Cancel culture: the road to obscurantism - 0 views
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In her view, Ancient Greece's blind master storyteller, Homer, and his works, were guilty of "indulging and spreading sexism, racism, ableism, and Western-centrism". She came to the conclusion that canceling the classics seemed to be the most effective way to make sure that today's young generation could not, again in her view, be poisoned by the entirely fictional and mythical "sins" of Odysseus, Menelaus, and Priam.
How We Made AI As Racist and Sexist As Humans - 0 views
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The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.
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Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.
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