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

Spiekermann, Kai and Slavny, Adam and Axelsen, David and Lawford-Smith, Holly (2021) 'Big Data Justice: A Case for Regulating the Global Information Commons.' The Journal of Politics, Online (2). pp. 577-588. ISSN 0022-3816

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The advent of artificial intelligence (AI) challenges political theorists to think about data ownership and policymakers to regulate the collection and 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.

Item Type: Article
Uncontrolled Keywords: big data, artificial intelligence, machine learning, data ownership, inequality; big data; artifical intelligence; machine learning; data ownership; inequality; consent
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Government, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Nov 2019 16:44
Last Modified: 18 Aug 2022 11:45

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