Paul L. Caron

Wednesday, March 8, 2023

Blue J Predicts: The Intersection Between Tax Credits And Trade Or Business

Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Christopher Yan (Senior Legal Research Associate, Blue J Legal), The Intersection Between Tax Credits and Trade or Business, 178 Tax Notes Fed. 689 (Jan. 30, 2022):

Tax Notes Federal (2022)In last month’s installment of Blue J Predicts [The Rise Of The Robotic Tax Analyst], we reflected on the state of predictive tax analytics in 2023 and the long-term role of artificial intelligence in tax analysis, drawing on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). This month, we revisit a prediction made in February 2022, in which we analyzed the appeal of the Tax Court’s decision in Olsen before the Tenth Circuit [Blue J Predicts With 86%-95% Confidence That 10th Circuit Will Find That Taxpayer In Olsen Was Not Engaged In A Trade Or Business].  A central dispute in the case involved determining whether the Olsens’ activity constituted a trade or business — a crucial question with significant financial consequences for deductions, credits, exemptions, disqualifications, and penalties. The Tax Court held for the commissioner, deciding that the taxpayers were not engaged in a trade or business, and thus not entitled to specific deductions, credits, and exemptions.

Using Blue J’s machine-learning algorithm, we predicted with greater than 95 percent confidence that the Tenth Circuit would hold for the commissioner. Even when adopting the most favorable characterization of the facts presented by the taxpayer, Blue J’s algorithm still predicted with 86 percent confidence that the commissioner’s position would prevail, based solely on the position taken in the taxpayer’s submissions.

Our prediction proved accurate when the Tenth Circuit affirmed the Tax Court’s decision that the taxpayers were not engaged in a trade or business. This outcome demonstrates the effectiveness of machine-learning models in analyzing legal issues, particularly regarding trade or business status, an area in which the case law is extensive and the fact-intensive inquiry is complex. It also emphasizes the importance of understanding the trade or business question and highlights the potential of machine learning in evaluating the strength of legal arguments.

Here we provide a comprehensive examination of the facts and circumstances of the case and delve into the Tenth Circuit’s analysis. We then compare the Tenth Circuit’s reasoning with the insights and predictions generated by our machine-learning model. In doing so, we provide an in-depth understanding of the case and the role of machine learning in legal analysis.


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