Big Data Justice: A Case for Regulating The Global Information Commons

(WIth Adam Slavny, David Axelsen, and Holly Lawford-Smith). Working paper. PDF.

Abstract

The advent of artificial intelligence (AI) challenges political theorists to think about data ownership and policymakers to regulate the use of public data. AI producers benefit from free public data for training their systems while retaining the profits. We argue against the view that the use of public data must be free. The proponents of unconstrained use point out that consuming data does not diminish its quality and that information is in ample supply. Therefore, they suggest, publicly available data should be free. We present two objections. First, allowing free data use promotes unwanted inequality. Second, contributors of information did not and could not anticipate that their contribution would be used to train AI systems. Our argument implies that managing the ‘global information commons’ and charging for extensive data use is permissible and desirable. We discuss policy implications and propose a progressive data use tax to counter the inequality arising.