Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems

13 Pages Posted: 13 Aug 2020 Last revised: 10 Mar 2021

See all articles by A. Feder Cooper

A. Feder Cooper

Cornell University - Department of Computer Science

Karen Levy

Cornell University

Christopher De Sa

Cornell University - Department of Computer Science

Date Written: July 13, 2020

Abstract

In this paper we discuss the trade-off between accuracy and efficiency in distributed machine learning (ML) systems and analyze its resulting policy considerations. This trade-off is in fact quite common in multiple disciplines, including law and medicine, and it applies to a wide variety of sub-fields within computer science. Accuracy and efficiency trade-offs have unique implications in ML algorithms because, being probabilistic in nature, such algorithms generally exhibit error tolerance. After describing how the trade-off takes shape in real-world distributed computing systems, we show the interplay between such systems and ML algorithms, explaining in detail how accuracy and efficiency interact particularly in distributed ML systems. We close by making specific calls to action for approaching regulatory policy for the emerging technology of real-time distributed ML systems.

Keywords: machine learning systems, law

JEL Classification: K20

Suggested Citation

Cooper, A. Feder and Levy, Karen and De Sa, Christopher, Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems (July 13, 2020). Available at SSRN: https://ssrn.com/abstract=3650497 or http://dx.doi.org/10.2139/ssrn.3650497

A. Feder Cooper (Contact Author)

Cornell University - Department of Computer Science ( email )

United States

Karen Levy

Cornell University ( email )

Ithaca, NY 14853
United States

Christopher De Sa

Cornell University - Department of Computer Science ( email )

United States

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