Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions

16 Pages Posted: 10 Jan 2019 Last revised: 19 Aug 2019

See all articles by Deirdre K. Mulligan

Deirdre K. Mulligan

University of California, Berkeley - School of Information

Daniel Kluttz

University of California, Berkeley School of Information

Nitin Kohli

UC Berkeley School of Information

Date Written: July 7, 2019

Abstract

Algorithmic systems, particularly those based on machine learning, are increasingly being used to help us reason and make decisions. Effective systems that also align with societal values require not only designs that foster in-the-moment human engagement with such systems but also governance models that support ongoing critical engagement with system processes and outputs. Using the case of expert decision-support systems, we introduce the concept of contestability. We argue that contestability has distinct advantages over transparency and explainability, two policy objectives often offered as antidotes to the challenges posed by black-box algorithmic systems. We then discuss contestable design and governance principles as applied to clinical decision support (CDS) systems used by health-care professionals to aid medical decisions. We explain current governance frameworks around the use of these systems — particularly laws and professional standards — and point out their limitations. We argue that approaches focused on contestability better promote professionals’ continued, active engagement with algorithmic systems than current frameworks.

Keywords: professions, algorithmic systems, automated decision support, contestability, explainability, transparency, human-computer-interaction, governance

Suggested Citation

Mulligan, Deirdre K. and Kluttz, Daniel and Kohli, Nitin, Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions (July 7, 2019). Available at SSRN: https://ssrn.com/abstract=3311894 or http://dx.doi.org/10.2139/ssrn.3311894

Deirdre K. Mulligan (Contact Author)

University of California, Berkeley - School of Information ( email )

102 South Hall
Berkeley, CA 94720-4600
United States

Daniel Kluttz

University of California, Berkeley School of Information ( email )

102 South Hall #4600
Berkeley, CA 94720-4600
United States

Nitin Kohli

UC Berkeley School of Information ( email )

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