The development of artificial intelligence (AI) models that are capable of predicting the decisions of prominent courts – most notably the European Court of Human Rights and United States Supreme Court – provides us with an opportunity to revisit important jurisprudential debates regarding the quest for legal certainty. Through providing clear distinctions within formalistic jurisprudence, and its, subsequent, realist critique; this thesis seeks to analyze legal decision-making and its relationship with artificial intelligence. I argue that, AI’s deterministic nature and its support for the law being an “entirely self-contained process” does lend some credence to certain jurisprudential arguments. However, this should not be misconstrued as support for a formalistic approach towards legal certainty. Rather, AI’s empirical attempt at understanding the contributing factors that create a legal decision, reaffirms a functional understanding of the law. Moreover, through highlighting the definitional issues of AI, its problematic facets and existing case law, this thesis seeks to provide a more nuanced comprehension of AI within the legal industry. I further argue that, inversed-AI models possess inherent inadequacies and, consequently, are at fundamental odds with the decision-making process; thus, preventing them from being reliable indicators of AI’s potential in the legal process. This is supported by the emergence of legal frameworks, the “General Data Protection Regulation” and “Loi de Programmation” in particular, that stipulate “explainability” and “understandability” as necessary benchmarks for the use of AI.


School of Global Affairs and Public Policy


Law Department

Degree Name

MA in International Human Rights Law

Graduation Date

Fall 12-20-2021

Submission Date


First Advisor

Hani Sayed

Committee Member 1

Jason Beckett

Committee Member 2

Thomas Skouteris


63 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Included in

Jurisprudence Commons