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Algorithmic Governance in Computational Antitrust—a Brief Outline of Alternatives for Policymakers
Author(s) -
Marcela Mattiuzzo,
Henrique Felix Machado
Publication year - 2022
Language(s) - English
DOI - 10.51868/11
Subject(s) - discretion , safeguard , corporate governance , equity (law) , law and economics , enforcement , consent decree , competition (biology) , process (computing) , big data , competition law , law enforcement , state (computer science) , economics , political science , industrial organization , business , computer science , law , microeconomics , monopoly , management , ecology , biology , operating system , algorithm
Computational antitrust consists of empowering competition authorities with modern techniques of artificial intelligence (AI), machine learning (ML), big data and associated solutions in the hope of enhancing antitrust enforcement and equipping it to deal with the dynamics of increasingly digitized markets. However, such power may come with risks of crossing the red lines posed by constitutional and public law requirements that limit and balance State discretion, such as fundamental due process rights, equity, and personal data protection. In this article, we explore some contributions from the algorithmic governance literature to help mitigate those risks and safeguard future computational antitrust solutions.

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