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      The Society of Algorithms

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      Annual Review of Sociology
      Annual Reviews

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          Abstract

          The pairing of massive data sets with processes—or algorithms—written in computer code to sort through, organize, extract, or mine them has made inroads in almost every major social institution. This article proposes a reading of the scholarly literature concerned with the social implications of this transformation. First, we discuss the rise of a new occupational class, which we call the coding elite. This group has consolidated power through their technical control over the digital means of production and by extracting labor from a newly marginalized or unpaid workforce, the cybertariat. Second, we show that the implementation of techniques of mathematical optimization across domains as varied as education, medicine, credit and finance, and criminal justice has intensified the dominance of actuarial logics of decision-making, potentially transforming pathways to social reproduction and mobility but also generating a pushback by those so governed. Third, we explore how the same pervasive algorithmic intermediation in digital communication is transforming the way people interact, associate, and think. We conclude by cautioning against the wildest promises of artificial intelligence but acknowledging the increasingly tight coupling between algorithmic processes, social structures, and subjectivities.

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          Most cited references143

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          Dissecting racial bias in an algorithm used to manage the health of populations

          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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            Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

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              Labor and Monopoly Capital

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                Author and article information

                Journal
                Annual Review of Sociology
                Annu. Rev. Sociol.
                Annual Reviews
                0360-0572
                1545-2115
                July 31 2021
                July 31 2021
                : 47
                : 1
                : 213-237
                Affiliations
                [1 ]School of Information and Department of Sociology, University of California, Berkeley, California 94720, USA;,
                Article
                10.1146/annurev-soc-090820-020800
                54b04b32-cae6-4bf8-9a80-48ce7838e3be
                © 2021
                History

                Social & Information networks,Data structures & Algorithms,Performance, Systems & Control,Robotics,Neural & Evolutionary computing,Artificial intelligence

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