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Predatory predictions and the ethics of predictive analytics

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  • Kirsten Martin

Abstract

In this paper, I critically examine ethical issues introduced by predictive analytics. I argue firms can have a market incentive to construct deceptively inflated true‐positive outcomes: individuals are over‐categorized as requiring a penalizing treatment and the treatment leads to mistakenly thinking this label was correct. I show that differences in power between firms developing and using predictive analytics compared to subjects can lead to firms reaping the benefits of predatory predictions while subjects can bear the brunt of the costs. While profitable, the use of predatory predictions can deceive stakeholders by inflating the measurement of accuracy, diminish the individuality of subjects, and exert arbitrary power. I then argue that firms have a responsibility to distinguish between the treatment effect and predictive power of the predictive analytics program, better internalize the costs of categorizing someone as needing a penalizing treatment, and justify the predictions of subjects and general use of predictive analytics. Subjecting individuals to predatory predictions only for a firms' efficiency and benefit is unethical and an arbitrary exertion of power. Firms developing and deploying a predictive analytics program can benefit from constructing predatory predictions while the cost is borne by the less powerful subjects of the program.

Suggested Citation

  • Kirsten Martin, 2023. "Predatory predictions and the ethics of predictive analytics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 531-545, May.
  • Handle: RePEc:bla:jinfst:v:74:y:2023:i:5:p:531-545
    DOI: 10.1002/asi.24743
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    References listed on IDEAS

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    1. Kirsten Martin, 2019. "Ethical Implications and Accountability of Algorithms," Journal of Business Ethics, Springer, vol. 160(4), pages 835-850, December.
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    5. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    6. Britt Paris & Rebecca Reynolds & Catherine McGowan, 2022. "Sins of omission: Critical informatics perspectives on privacy in e‐learning systems in higher education," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 708-725, May.
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