Using Machine Learning to Predict Reasonable Notice Awards

Anthony Niblett (Univ. of Toronto, Faculty of Law)

Abstract: Under Canadian employment law, when an employee is terminated without cause, they are entitled to a reasonable notice period from their employer – or payment in lieu of this notice period. What is “reasonable” for any given worker is, however, determined on a case-by-case basis. Employers and lawyers currently tend to rely heavily on length of service when seeking to predict how a court might decide reasonable notice cases. These rules of thumb are very weak predictors. The average prediction error generated by these simple heuristics is large. Machine learning techniques vastly outperform these simple heuristics in terms of predicting the outcome of reasonable notice cases. I use machine learning algorithms that have been developed for the purpose of prediction. Familiar estimation techniques, such as ordinary least squares, do (of course) offer simple ways to generate predictions. But they do not offer the best means of predicting out-of-sample. I explore the power of boosted decision trees, random forests, and neural net regression to predict how a court will decide cases not included in my training data. Boosted decision trees, tuned by depth, offer the best means of predicting court outcomes in my sample. In terms of explanatory power, boosted decision trees outperform the linear prediction model by as much as 12% and reduce the prediction error by as much as 15%. Random forest algorithms also outperform the linear prediction model, but the performance is weaker than with boosted decision trees. Neural net regression algorithms do not outperform the linear model, which is not surprising, given the size of our dataset.