Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Christopher Yan (Senior Legal Research Associate, Blue J Legal), Using Machine Learning to Evaluate the Existence of a Trade or Business: Olsen, 174 Tax Notes Fed. 1231 (Feb. 28, 2021):
In this article, Alarie and Yan examine how machine learning can be used to assess the strength of the taxpayer’s position in the appeal of the Tax Court’s decision in Olsen [v. Commissioner, T.C. Memo. 2021-41 (Apr. 6, 2021)]. ...
When the Tax Court’s opinion was released, Blue J’s algorithm originally predicted with over 95 percent confidence that a court would rule that the taxpayer did not engage in a trade or business after considering all the factors. Blue J made this prediction based on the Tax Court’s finding that the taxpayer did not experience any profitable years since inception.
However, even if we adopt the most favorable version of the taxpayer’s appeal position to include tax benefits as part of profits and we assume that each of the tax years was profitable, Blue J’s algorithm predicts with 86 percent confidence that a court would still likely rule that the taxpayer did not engage in a trade or business.
Table 1 illustrates the effect of profitability on Blue J’s prediction on whether a court is likely to find that the activity constitutes a trade or business.
Using a machine-learning model can provide valuable insight, especially in legal areas with a sizable body of case law and a fact-intensive inquiry. Our analysis reveals that the taxpayer’s chances of successfully appealing the Tax Court’s trade or business determination in Olsen are not high, even if we entirely accept the taxpayer’s characterization of the issues. While advances in computing power have come a long way, they are not ready to replace human decision-making, and tax practitioners remain in the driver’s seat when it comes to novel arguments and clever characterizations. Still, tax practitioners can benefit from speeding up the process of determining which arguments to focus on and leverage Blue J’s unique cataloging method to accelerate research and compare cases that may seem similar on their surface but meaningfully differ in ways that are not immediately obvious.
Prior TaxProf Blog coverage: