Understanding Transparency in Algorithmic Accountability

Forthcoming in Cambridge Handbook of the Law of Algorithms, ed. Woodrow Barfield, Cambridge University Press (2020).

U of Colorado Law Legal Studies Research Paper No. 20-34

28 Pages Posted: 1 Jul 2020 Last revised: 24 Jul 2020

See all articles by Margot E. Kaminski

Margot E. Kaminski

University of Colorado Law School; Yale University - Yale Information Society Project; Yale University - Law School; University of Colorado at Boulder - Silicon Flatirons Center for Law, Technology, and Entrepreneurship

Date Written: June 8, 2020

Abstract

Transparency has been in the crosshairs of recent writing about accountable algorithms. Its critics argue that releasing data can be harmful, and releasing source code won’t be useful. They claim individualized explanations of artificial intelligence (AI) decisions don’t empower people, and instead distract from more effective ways of governing. While criticizing transparency’s efficacy with one breath, with the next they defang it, claiming corporate secrecy exceptions will prevent useful information from getting out.

This chapter bucks the tide. Transparency is necessary, if not sufficient, for building and governing accountable algorithms. But for transparency to be effective, it has to be designed. It can’t be sprinkled on like seasoning; it has to be built into a regulatory system from the onset. And determining the who, what, when, and how of transparency requires first addressing the question of why.

Building on my work elsewhere, I thus begin by discussing the rationales behind regulating algorithmic decision-making, or decision-making by AI. I discuss the growing awareness in the literature that the object of regulation is not the technology of the algorithm in isolation, but includes the human systems around it. I then outline a taxonomy of transparency for accountable algorithms, building on the work of earlier authors and my own research on the European Union’s General Data Protection Regulation (GDPR).

Keywords: AI, Algorithmic Accountability, GDPR

Suggested Citation

Kaminski, Margot E. and Kaminski, Margot E., Understanding Transparency in Algorithmic Accountability (June 8, 2020). Forthcoming in Cambridge Handbook of the Law of Algorithms, ed. Woodrow Barfield, Cambridge University Press (2020)., U of Colorado Law Legal Studies Research Paper No. 20-34, Available at SSRN: https://ssrn.com/abstract=3622657

Margot E. Kaminski (Contact Author)

University of Colorado Law School ( email )

401 UCB
Boulder, CO 80309
United States

Yale University - Yale Information Society Project ( email )

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Yale University - Law School ( email )

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University of Colorado at Boulder - Silicon Flatirons Center for Law, Technology, and Entrepreneurship ( email )

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Boulder, CO
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