Demographic skews in training data create algorithmic errors
Women and people of colour are underrepresented and depicted with stereotypes
ALGORITHMIC BIAS is often described as a thorny technical problem. Machine-learning models can respond to almost any pattern—including ones that reflect discrimination. Their designers can explicitly prevent such tools from consuming certain types of information, such as race or sex. Nonetheless, the use of related variables, like someone’s address, can still cause models to perpetuate disadvantage.
This article appeared in the Graphic detail section of the print edition under the headline “Bias in, bias out”
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