Predicting Employment Notice Period with Machine Learning: Promises and Limitations - Dahan, Samuel; Touboul, Jonathan; Lam, Jason; Sfedj, Dan (McGill Law Journal / Revue de droit de McGill)

(untitled) (McGill Law Journal / Revue de droit de McGill, 2020)
Rapid advances in data analysis techniques—particularly for predictive algorithms—have opened the door for radically new perspectives on legal practice and access to justice. Several firms in North America, Asia, and Europe have set out to use machine-learning techniques to generate legal predictions, raising concerns regarding ethics, reliability and limits on prediction accuracy, and potential impact on case law development. To explore these opportunities and challenges, we consider in depth one of the most litigated issues in Canada: wrongful termination disputes and, more specifically, the question of reasonable notice determination. Beyond the thorough analysis of this question, this paper is also intended to act as a road map for non-technicians (and especially lawyers) on the application of artificial intelligence (AI) methods, illustrating both their potential benefits and limitations in other areas of dispute resolution.To achieve these results, we first created a large dataset by annotating historic cases related to employment termination. This dataset proved useful for assessing the predictability of reasonable notice of termination, that is, the accuracy and precision of AI predictions. In particular, it helped identify the degree of inconsistency in notice period cases, incidentally exposing the limitations of legal predictions. We then developed predictive algorithms to estimate notice periods based on details of the employment period and investigated their accuracy and performance. Moreover, we thoroughly analyzed these algorithms to better understand the judicial process, and in particular to quantify the weight and influence of case-specific features in the determination of reasonable notice. Finally, we closely analyzed cases that were poorly predicted by the algorithms to understand the judicial decision-making process and identify inconsistencies—a strategy that will ultimately yield a deeper practical understanding of case law.This project will open the door to the development of an access-to-justice project and will provide users with an open-access platform for employment legal help (www.MyOpenCourt.org).