Adjudicating Algorithms: Accountability in Regulation of Surveillance, Privacy, and Discrimination

Authors

Peter Margulies, Roger Williams University School of LawFollow

Abstract

The movement for accountable algorithms has attained critical mass. That momentum includes a range of areas where the collection of data plays a key role, including privacy, online disinformation, surveillance, and screening for credit, housing, employment, and government benefits. For example, the White House has released an Artificial Intelligence (AI) Bill of Rights that outlines standards and recourse for a host of AI applications that touch human needs and endeavors. Assessments, disclosure, and procedures for filing complaints about abuse are frequent features in this turn toward accountability. However, at least in the United States, accountability is still a set of tropes, rather than a consistent approach across domains. Even among scholars proposing reforms, disagreements and omissions are frequent calling cards. Scholars are divided about the utility of procedures for review of individual complaints, as ongoing controversy over the value of the Meta Oversight Board (OB) reveals. In addition, few, if any, accountability frameworks respond to the ubiquity of functional trade-offs between different values or even within values. For example, attempts to make algorithms more transparent can reduce their accuracy. This Article outlines a consistent approach, which the Article calls stewardship, for regulation of data abuse.

Keywords

Data Protection, Data Processing and Use, Legislation, Comparative and Foreign Law, Information Privacy, Science and Technology Law, Communications Law, Military Law and Justice

Disciplines

Communications Law | Comparative and Foreign Law | Law | Legislation | Military, War, and Peace | Science and Technology Law

Recommended Citation

Peter Margulies, Adjudicating Algorithms: Accountability in Regulation of Surveillance, Privacy, and Discrimination, 45 Cardozo L. Rev. 1 (2023).
Available at: https://larc.cardozo.yu.edu/clr/vol45/iss1/3