Wednesday, March 1, 2017
Benjamin Alarie (Toronto) presents Using Machine Learning to Predict Outcomes in Tax Law (with Anthony Niblett (Toronto) & Albert H. Yoon (Toronto)) at Toronto today as part of its James Hausman Tax Law and Policy Workshop Series:
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. A final section concludes.