Associate Professor Margot Kaminski delivered the 11th Annual Judge Stephanie K. Seymour Distinguished Lecture in Law, "Binary Governance: A Two-Part Approach to Accountable Algorithms," at the University of Tulsa College of Law on Feb. 21.
Event abstract:
Algorithms are used to make significant decisions about individuals, from credit determinations to hiring. But they are largely unregulated under U.S. law. A growing body of literature examines algorithmic accountability: how to make black-box algorithms–or really, their programmers and the companies that employ them–transparent and accountable to the humans whose lives they affect. The lecture will identify three categories of concerns behind calls for regulating algorithmic decision-making: dignitary, justificatory, and instrumental. Dignitary (including autonomy) concerns lead to proposals that we regulate algorithms to protect human dignity; justificatory concerns caution that we must assess the legitimacy of algorithmic reasoning, and instrumental concerns lead to calls for regulation to prevent consequent problems such as error and bias. No one regulatory approach can effectively address all three categories. Professor Kaminski will propose a two-pronged approach to algorithmic governance: a system of individual due process rights, combined with collaborative governance (the use of private-public partnerships). Only through this binary approach can we effectively address all three categories of concerns raised by algorithmic, or AI, decision-making. The interplay between the two approaches will be complex; sometimes the two systems will be complementary, and at other times they will be in tension. Professor Kaminski will identify that the EU’s General Data Protection Regulation (GDPR) is, in fact, such a binary system. This Article is the first to identify and explore, at any length, the extensive collaborative governance aspects of the GDPR, and how they interact with its individual rights regime. Understanding the GDPR in this way both illuminates its strengths and weaknesses and provides a model for how to construct a better binary governance regime for algorithmic, or AI, decision making.