Clearing Opacity through Machine Learning

38 Pages Posted: 8 Mar 2020 Last revised: 12 Feb 2021

See all articles by W. Nicholson Price II

W. Nicholson Price II

University of Michigan Law School

Arti K. Rai

Duke University School of Law; Duke Innovation & Entrepreneurship Initiative

Date Written: February 12, 2020


Artificial intelligence and machine learning provide powerful tools in many fields ranging from criminal justice to human biology to climate change. Part of the power of these tools arises from their ability to make predictions and glean useful information about complex real-world systems without the need to understand the workings of those systems.

But these machine-learning tools are often as opaque as the underlying systems, whether because they are complex, nonintuitive, deliberately kept secret, or a synergistic combination of those three factors. A burgeoning literature addresses challenges arising from the opacity of machine-learning systems. This literature has largely focused on the benefits and difficulties of providing information to lay individuals, such as citizens impacted by algorithm-driven government decisions.

In this Article, we explore the potential of machine learning to clear opacity — that is, to help drive scientific understanding of the frequently complex and nonintuitive real-world systems that machine-learning algorithms examine. Using multiple examples drawn from cutting-edge scientific research, we argue machine-learning algorithms can advance fundamental scientific knowledge and that deliberate secrecy around machine-learning tools restricts that learning enterprise. We examine why developers are likely to keep machine-learning systems secret, and the costs and benefits of that secrecy. Finally, we draw on the innovation policy toolbox to suggest ways to reduce secrecy so that machine learning can help us not only to interact with complex, non-intuitive real-world systems but also to understand them.

Keywords: artificial intelligence, secrecy, opacity, machine learning, basic research, innovation policy, trade secrets

Suggested Citation

Price II, William Nicholson and Rai, Arti Kaur, Clearing Opacity through Machine Learning (February 12, 2020). 106 Iowa L. Rev. 775 (2021), Duke Law School Public Law & Legal Theory Series No. 2020-13, U of Michigan Public Law Research Paper No. 663, U of Michigan Law & Econ Research Paper No. 20-016, Available at SSRN: or

William Nicholson Price II (Contact Author)

University of Michigan Law School ( email )

625 South State Street
Ann Arbor, MI 48109-1215
United States

Arti Kaur Rai

Duke University School of Law ( email )

210 Science Drive
Box 90362
Durham, NC 27708
United States

Duke Innovation & Entrepreneurship Initiative ( email )

215 Morris St., Suite 300
Durham, NC 27701
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics