Turning Standards into Rules Part 1: Using Machine Learning to Predict Tax Outcomes

Abstract

Technological advancements in the last 10 years have made it possible to explore the application of data driven tools to the legal field. The problem, however, is that law is full of grey areas. Tax law in particular can be a very uncertain field, often precisely because of the many rules and standards that are supposed to provide lawyers and judges with guidance. Rules are a challenge because of their intricate specificity. Standards, on the other hand, are difficult because of their amorphousness. In terms of standards, tax lawyers must characterize relationships, instruments, and entities to help their clients comply with the law. Prediction regarding characterization is an essential legal skill: in order to advise on compliance or prepare for litigation, tax lawyers must be able to predict how the courts will characterize their client’s situation. However, legal predictions are limited by human judgment. Even the most careful lawyer’s predictions can be inaccurate in any number of ways: they may be based on overly broad rules of thumb, biased by individual experiences, or influenced by the interests of clients. But recent advances in machine learning provide lawyers with an opportunity to use these powerful new tools to support their predictions. By analyzing the facts and outcomes of past cases, machine learning algorithms can find hidden patterns in the existing data to predict the outcome of new scenarios.

Keywords: machine-learning, tax, legal prediction

JEL Classification: H2, H20, H29