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How to regulate algorithmic decision‐making: A framework of regulatory requirements for different applications

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  • Tobias D. Krafft
  • Katharina A. Zweig
  • Pascal D. König

Abstract

Algorithmic decision‐making (ADM) systems have come to support, pre‐empt or substitute for human decisions in manifold areas, with potentially significant impacts on individuals' lives. Achieving transparency and accountability has been formulated as a general goal regarding the use of these systems. However, concrete applications differ widely in the degree of risk and the accountability problems they entail for data subjects. The present paper addresses this variation and presents a framework that differentiates regulatory requirements for a range of ADM system uses. It draws on agency theory to conceptualize accountability challenges from the point of view of data subjects with the purpose to systematize instruments for safeguarding algorithmic accountability. The paper furthermore shows how such instruments can be matched to applications of ADM based on a risk matrix. The resulting comprehensive framework can guide the evaluation of ADM systems and the choice of suitable regulatory provisions.

Suggested Citation

  • Tobias D. Krafft & Katharina A. Zweig & Pascal D. König, 2022. "How to regulate algorithmic decision‐making: A framework of regulatory requirements for different applications," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 119-136, January.
    • Handle: RePEc:wly:reggov:v:16:y:2022:i:1:p:119-136
    DOI: 10.1111/rego.12369
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    References listed on IDEAS

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    Cited by:

    1. Jella Pfeiffer & Julia Gutschow & Christian Haas & Florian Möslein & Oliver Maspfuhl & Frederik Borgers & Suzana Alpsancar, 2023. "Algorithmic Fairness in AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 209-222, April.

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