Algorithmic propagation: How the data-platform regulatory framework may increase bias in content moderation, Forthcoming in: Caterina Sganga & Tatiana Eleni Synodinou (Eds), Flexibilities in Copyright Law, Routledge, 2025

23 Pages Posted: 19 Aug 2024

See all articles by Thomas Margoni

Thomas Margoni

Centre for IT & IP Law (CiTiP), Faculty of Law - KU Leuven

João Pedro Quintais

University of Amsterdam - Institute for Information Law (IViR)

Sebastian Felix Schwemer

University of Copenhagen, Centre for Information and Innovation Law (CIIR); University of Oslo, Norwegian Research Center for Computers and Law (NRCCL)

Date Written: August 01, 2024

Abstract

This chapter offers a reflection on the topic of content moderation and bias mitigation measures in copyright law. It explores the possible links between conditional data access regimes and content moderation performed through data-intensive technologies such as fingerprinting and machine learning algorithms. In recent years, various pressing questions surrounding automated decision-making and their legal implications materialised. In European Union (EU) law, answers were provided through different regulatory interventions often based on specific legal categories, rights, and foundations contributing to the increasing complexity of interacting frameworks. Within this broader background, the chapter discusses whether current EU copyright rules may have the effect of favouring what we call the propagation of bias present in input data to the output algorithmic tools employed for content moderation. The chapter shows that a reduced availability and transparency of training data often leads to negative effects on access, verification and replication of results. These are ideal conditions for the development of bias and other types of systematic errors to the detriment of users' rights. The chapter discusses a number of options that could be employed to mitigate this undesirable effect and contextually preserve the many fundamental rights at stake.

Keywords: copyright, content moderation, generative AI, Art. 17, DSA, AI Act, bias

Suggested Citation

Margoni, Thomas and Quintais, João Pedro and Schwemer, Sebastian Felix, Algorithmic propagation: How the data-platform regulatory framework may increase bias in content moderation, Forthcoming in: Caterina Sganga & Tatiana Eleni Synodinou (Eds), Flexibilities in Copyright Law, Routledge, 2025
(August 01, 2024). Available at SSRN: https://ssrn.com/abstract=4913758

Thomas Margoni

Centre for IT & IP Law (CiTiP), Faculty of Law - KU Leuven ( email )

Brussels
Belgium

João Pedro Quintais (Contact Author)

University of Amsterdam - Institute for Information Law (IViR) ( email )

Rokin 84
Amsterdam, 1012 KX
Netherlands

HOME PAGE: http://https://www.ivir.nl/profile/quintais/

Sebastian Felix Schwemer

University of Copenhagen, Centre for Information and Innovation Law (CIIR) ( email )

Karen Blixens Plads 16
Copenhagen, 2300
Denmark

HOME PAGE: http://jura.ku.dk/schwemer

University of Oslo, Norwegian Research Center for Computers and Law (NRCCL) ( email )

Karl Johans gt. 47
Domus Academica
Oslo, Oslo 0130
Norway

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