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AI can be sexist and racist — it’s time to make it fair

Computer scientists must identify sources of bias, de-bias training data and develop artificial-intelligence algorithms that are robust to skews in the data, argue James Zou and Londa Schiebinger.
  1. James Zou

    1. James Zou is assistant professor of biomedical data science and (by courtesy) of computer science and of electrical engineering, Stanford University, California, USA.

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  2. Londa Schiebinger

    1. Londa Schiebinger is the John L. Hinds Professor of History of Science and director of Gendered Innovations in Science, Health & Medicine, Engineering, and Environment, Stanford University, California, USA.

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Algorithms trained on biased data sets often recognize only the left-hand image as a bride. Credit: Left: iStock/Getty; Right: Prakash Singh/AFP/Getty

When Google Translate converts news articles written in Spanish into English, phrases referring to women often become ‘he said’ or ‘he wrote’. Software designed to warn people using Nikon cameras when the person they are photographing seems to be blinking tends to interpret Asians as always blinking. Word embedding, a popular algorithm used to process and analyse large amounts of natural-language data, characterizes European American names as pleasant and African American ones as unpleasant.

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Nature 559, 324-326 (2018)

doi: https://doi.org/10.1038/d41586-018-05707-8

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