THE JAMES HAUSMAN TAX LAW & POLICY WORKSHOP SERIES
presents
Benjamin Alarie
University of Toronto Faculty of Law
Using Machine Learning to Predict Outcomes in Tax Law
Wednesday, March 1, 2017
12:30 - 1:45
Solarium (Room FA2), Falconer Hall
84 Queen's Park
Recent advances in artificial intelligence and machine learning have bolstered the predictive power of data analytics. Research tools based on these developments will soon be commonplace. For the past two years, the three of us have been working on a project called Blue J Legal. We started with a view to understanding how machine learning techniques can be used to better predict legal outcomes. In this paper, we report on our experiences so far. The paper is set out in four parts. In Part 1, we discuss the importance of prediction. In many fields, humans are outperformed by mechanical and algorithmic prediction. We explore this phenomenon and conclude that the legal field is no different. In Part 2, we discuss recent advances in machine learning that have generated powerful tools for prediction. These new methods outperform traditional statistical techniques in predicting outcomes. In Part 3, we describe the Blue J Legal project. We discuss how Blue J Legal is using these machine learning technologies to provide predictions in grey areas of tax law. We provide a number of examples to illustrate the strength of these predictions. In part 4, we discuss the broader possibilities for technologies such as those powering Blue J Legal. We foresee a world where information about legal rights and responsibilities is more affordable; where the informational asymmetries that lead to wasteful expenditure on litigation is reduced; and where regulators use these tools to create a more effective and efficient administration of government.
Benjamin Alarie, M.A. (Toronto), J.D. (Toronto), LL.M. (Yale) researches and teaches in taxation law and judicial decision-making. Before joining the Faculty of Law, Professor Alarie was a law clerk for Madam Justice Louise Arbour at the Supreme Court of Canada (2003-2004). Over the years his publications have appeared in numerous academic journals, including the American Business Law Journal, the British Tax Review, the Canadian Business Law Journal, the Canadian Tax Journal, the Osgoode Hall Law Journal and the University of Toronto Law Journal. His research has been funded by the Social Sciences and Humanities Research Council, the Canadian Foundation for Innovation, and the Ontario Ministry of Research and Innovation. He is coauthor of several editions of Canadian Income Tax Law (LexisNexis) and was awarded the Alan Mewett QC Prize for Excellence by the JD class of 2009.
For more workshop information, please contact Nadia Gulezko at n.gulezko@utoronto.ca.
