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Data-Driven Unfair Competition in Digital Markets

Chirita, Anca Daniela



This article expands the controversial catalogue of abusive practices to account for new types of data-driven unfair practices, such as data leveraging and combination, behavioral and personalized discrimination, excessive and algorithmic pricing, and the misuse of data, due to inherent conflicts of interest, driven by platform–based competition. It advances a novel understanding of the abuse of data, based on a human-centric approach to competition law. The latter applies fundamental human-rights principles to competition law, including non-discrimination, equality of opportunity, and the value of fairness for both consumers and entrepreneurs, as well as the freedoms of choice, consent, and entrepreneurial action, and consumers’ right to economic privacy. Transitioning from the above evolutionary trajectory, which moves from economic dependence on the power of gigantic monopolies toward the diminishing power of gatekeepers, this article raises pressing concerns about the power of predictive analytics to accomplish the yet unaccomplished mission of surveillance capitalism, including human dependence on robotics, machine learning, and manipulative algorithms.


Chirita, A. D. (2023). Data-Driven Unfair Competition in Digital Markets. Journal of Science & Technology Law, 29(2),

Journal Article Type Article
Acceptance Date Jan 22, 2023
Publication Date 2023
Deposit Date Jan 24, 2023
Publicly Available Date Oct 3, 2023
Journal Boston University Journal of Science & Technology Law
Publisher Boston University School of Law
Peer Reviewed Peer Reviewed
Volume 29
Issue 2
Public URL
Publisher URL
Related Public URLs

This file is under embargo due to copyright reasons.

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