Indeterminate antitrust concepts and Artificial Intelligence: the case of plausibility

Legal texts are replete with indeterminate concepts, i.e. words to which it is challenging to assign a definitive meaning. The interpretation of these terms, including the determination of their meaning, is thus an integral part of the work undertaken by legal experts. This paper seeks to investigate the potential for artificial intelligence (AI) to support or even supplant human effort in the realm of legal interpretation. Specifically, the paper examines this possibility through the lens of the concept of plausibility in antitrust law. The rationale for this focus is twofold. First, within antitrust discourse, the concept of plausibility is pertinent to both substantive and procedural law, which calls for various needs that interpreters—even artificial ones—should meet. Second, the influence of economic thought on antitrust law enables the interpretation of plausibility not only based on legal precedents and judges’ understanding of the ordinary world, but also on economic principles, which enables the development of two models of AI: one running on data, one informed to principles.