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AI’s social sciences deficit

Nature Machine Intelligence volume 1, pages 330–331 (2019)Cite this article

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To create less harmful technologies and ignite positive social change, AI engineers need to enlist ideas and expertise from a broad range of social science disciplines, including those embracing qualitative methods, say Mona Sloane and Emanuel Moss.

Computer scientists are building a vast array of machine learning systems (often called AI) that can perform human tasks reliably, making us believe that AI can be a judge, a shopkeeper, a chauffeur, a financial analyst, a receptionist, a security guard, a doctor or a paralegal. Many of us enjoy the fruits of this labour, from our e-mail folder that is kept clear of spam to the improved chances we have of detecting cancer early. But AI doesn’t make everybody’s life easier or safer. There is mounting evidence that AI can exacerbate inequality, perpetuate discrimination and inflict harm: Virginia Eubanks1 has demonstrated how automated systems in government services can increase stigma, exacerbate poverty and inflict harm based on social class; Safiya Noble2 has shown how search engines discriminate against women of colour; Joy Buolamwini and Timnit Gebru3 have provided evidence for discrimination in image data bases and automated gender classification systems; Wilson, Hoffman and Morgenstern4 have proven that there are higher error rates for pedestrians with darker skin tones in object detection systems; Bolukbasi et al.5 highlighted gender stereotypes in word embeddings; and Os Keyes6 has shown how automated gender recognition systems perpetuate violence against trans identities. These new streams of work show that as AI is increasingly used in the organization of society and its basic institutions, the stakes are high. It is impossible to build an equitable and prosperous future with decision-making machines that amplify historical patterns of oppression.

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Authors and Affiliations

  1. New York University, New York, NY, USA

    Mona Sloane

  2. The Graduate Center, CUNY, New York, NY, USA

    Emanuel Moss

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  1. Mona Sloane
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  2. Emanuel Moss
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Correspondence to Mona Sloane.

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Sloane, M., Moss, E. AI’s social sciences deficit. Nat Mach Intell 1, 330–331 (2019). https://doi.org/10.1038/s42256-019-0084-6

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