Predicting Employment Notice Period with Machine Learning: Promises and Limitations
Samuel Dahan and others, ‘Predicting Employment Notice Period with Machine Learning: Promises and Limitations’ (2020) 65 McGill Law Journal / Revue de droit de McGill 711.
33 Pages Posted: 4 Jun 2020 Last revised: 5 Aug 2021
Date Written: May 7, 2020
Rapid advances in data analysis techniques, particularly predictive algorithms, have opened radically new perspectives for legal practice and access to justice. Several firms in North America, Asia and Europe have set out to use machine-learning techniques to create automated 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, more specifically the question of reasonable notice calculation. Beyond the thorough analysis of this question, this paper is also intended as a road map for non-technicians, and especially lawyers, on the application of Artificial Intelligence (AI) methods, illustrating both its potential benefits and its limitations in other areas of dispute resolution. To achieve these results, we created a large data set by annotating all historic cases related to wrongful employment termination. This data set has proven useful to assess the predictability of reasonable notice, that is, the accuracy and precision of AI predictions. In particular, it helped to identify the degree of inconsistency and fluctuation in notice period cases, incidentally exposing the limitations of legal predictions. We then developed predictive algorithms to estimate notice periods given 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 AI algorithms in order to better understand the judicial decision 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 a larger- scale access-to-justice project, and will provide users with an open-access platform for notice calculation. In particular, the tool will help self-represented litigants to appreciate possible outcomes of litigation – in this case, reasonable notice – that is, the Best Alternative to a Negotiated Agreement (BATNA).
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