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Errors that do not fall at random
When an algorithm gets certain faces wrong more often, it is not a neutral bug: it is an incomplete "us."
Read the articleThread 02 / 07 · Stories
A machine learns from what we show it. And from what we forget to show it.
The thread
14 articles · each in four movements: a scene, a documented case, a voice, a question
Featured
When an algorithm gets certain faces wrong more often, it is not a neutral bug: it is an incomplete "us."
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From Morton's skulls to spirometers "corrected" by race, science long served hierarchy; today it is learning to undo it.
ReadIn Australia, an automated calculation wrongly claimed hundreds of thousands of debts from welfare recipients. A royal commission found it cruel and unlawful.
ReadIn 2018, hundreds of Montrealers helped write ten principles so that artificial intelligence serves everyone, without discrimination.
ReadIn Detroit, a Black father was wrongly arrested in front of his daughters because of software; his courage changed the police's rules.
ReadIn Montreal, a Mila program trains young Indigenous people in artificial intelligence so that tomorrow's technology carries their voices too.
ReadA U.S. health algorithm judged patients' needs by their past spending; Black patients, who received less care, therefore seemed less sick.
ReadOttawa requires its departments to assess the risks of their algorithms before using them, and to keep a human in the loop when the answer is no.
ReadThe RCMP used facial recognition software built on photos that the commissioners found had been collected illegally; even the police must know where their tools come from.
ReadIn Quebec, Law 25 requires businesses to say when a machine alone has made a decision, and to let the person explain themselves to someone.
ReadA company copied billions of faces online to sell them to police; four Canadian commissioners said no together.
ReadIn 2023, a Bloomberg analysis showed that an image generator amplified gender and skin-colour stereotypes. A tool that makes images also makes places for people.
ReadIn 2020, users showed that Twitter's automatic cropping favoured certain faces; the company measured the bias, then gave the choice back to people.
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