Lim on Can Computational Antitrust Succeed?

Daryl Lim (University of Illinois Chicago School of Law ; Fordham University – Fordham Intellectual Property Institute) has posted “Can Computational Antitrust Succeed?” (Stanford Computational Antitrust, Vol. 1, 2021) on SSRN. Here is the abstract:

Computational antitrust comes to us at a time when courts and agencies are underfunded and overwhelmed, all while having to apply indeterminate rules to massive amounts of information in fast-moving markets. In the same way that Amazon disrupted e-commerce through its inventory and sales algorithms and TikTok’s progressive recommendation system keeps users hooked, computational antitrust holds the promise to revolutionize antitrust law. Implemented well, computational antitrust can help courts curate and refine precedential antitrust cases, identify anticompetitive effects, and model innovation effects and counterfactuals in killer acquisition cases. The beauty of AI is that it can reach outcomes humans alone cannot define as “good” or “better” as the untrained neural network interrogates itself via the process of trial and error. The maximization process is dynamic, with the AI being capable of scouring options to optimize the best rewards under the given circumstances, mirroring how courts operationalize antitrust policy–computing the expected reward from executing a policy in a given environment. At the same time, any system is only as good as its weakest link, and computational antitrust is no exception. The synergistic possibilities that humans and algorithms offer depend on their interplay. Humans may lean on ideology as a heuristic when they must interpret the rule of reason according to economic theory and evidence. For this reason, it becomes imperative to understand, mitigate, and, where appropriate, harness those biases.

Finck on The Limits of the GDPR in the Personalisation Context

Michèle Finck (Max Planck Institute for Innovation and Competition; University of Oxford) has posted “The Limits of the GDPR in the Personalisation Context” (U. Kohl, J. Eisler (eds), Data-Driven Personalisation in Markets, Politics and Law, Cambridge University Press, 2021) on SSRN. Here is the abstract:

Personalisation is both driven by, and can produce, personal data, and thus it falls within the scope of the General Data Protection Regulation (‘GDPR’). As a consequence, the European data protection framework applies to data-driven personalisation. Yet, whereas there appears to be a general perception that data protection is suitable to function as a general legal framework for AI, it is important to remain realistic regarding both its opportunities and limitations. This chapter examines the application of certain elements of the GDPR to data-driven personalisation. There are hopes that the GPDR can serve as a general legal framework to govern the normative concerns that have emerged in relation to AI. It is, however, fundamentally inadequate to serve as a ‘general AI law’. Whereas the Regulation indeed applies to the processing of personal data, it would be erroneous to frame it as a general ‘AI law’ capable of addressing all normative concerns around personalisation.