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.