Paul L. Caron

Wednesday, July 12, 2023

Blue J Predicts: Conflicting Duties And The Trust Fund Recovery Penalty In Cashaw

Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Christopher Yan (Senior Legal Research Associate, Blue J Legal), A Reexamination of Cashaw, 179 Tax Notes Fed. 2197 (June 26, 2023):

Tax Notes Federal (2022)[W]e revisit the intriguing case of Cashaw [v. Commissioner, T.C. Memo. 2021-123 (Oct. 27, 2021)]. This case focused on Pamela Cashaw, an administrator for a financially distressed hospital, who the Tax Court determined was personally liable for a $173,000 trust fund recovery penalty (TFRP) under section 6672. The fundamental issue was whether Cashaw was a responsible person who had willfully failed to fulfill her legal obligation to remit employee payroll tax withholdings, also known as trust fund taxes, thereby warranting the imposition of a TFRP.

In our November article we used Blue J’s TFRP prediction algorithm to assess the likely outcome of an appeal [Cashaw: Conflicting Duties And The Trust Fund Recovery Penalty, 177 Tax Notes Fed. 1257 (Nov. 28, 2022)]. Blue J predicted with 86 percent confidence that the Fifth Circuit would affirm the Tax Court’s decision if it endorsed the Tax Court’s findings of fact. Our analysis also considered alternate scenarios and examined the circumstances under which the Tax Court’s decision might be reversed. That exercise highlighted the importance of scrutinizing various factors in the case.

Now fast-forward to May 31. The Fifth Circuit affirmed the Tax Court’s decision, reiterating Cashaw’s liability for the trust fund recovery penalties. That validated Blue J’s prediction that Cashaw was a responsible person who had willfully neglected to pay. The result from Blue J’s TFRP predictive model, trained on the facts of more than 375 court opinions from 1956 to 2022 (trained up to the date of the prediction), underscores the transformative power of machine learning in conducting nuanced legal analyses.

As we reexamine Cashaw, we not only delve into the context of TFRP and the factors in the appeal but also reflect on the role our machine-learning-based prediction played in the analysis of this case. The alignment of machine-identified factors with those factors that have been decisive on appeal generates support for broader discussions on the future of artificial intelligence in legal decision-making.

Notably, our Blue J Predicts contribution in May strayed from tradition to compare large language models like ChatGPT (versions GPT-3.5 and GPT-4) with our proprietary Ask Blue J, a chatbot specifically trained to accelerate tax research [The Rise of Generative AI for Tax Research, 179 Tax Notes 1609 (May 29, 2023)] This comparative analysis was intended to outline the benefits, potential concerns, and areas for improvement the deployment of generative AI to respond to complex tax law queries. Ask Blue J, which is engineered exclusively for tax law, generates responses derived from a meticulously curated tax document database.

As we return to our established tradition of revisiting predictions on pending cases this month, we embark on a fresh journey — investigating how generative AI can enhance the research process. While generative AI may not yet be mature enough to replace machine learning predictions in the litigation spotlight, we see its potential in aiding our research.

To illustrate its use, we have provided sample legal queries and responses below. Also, we have dedicated a section to dissecting the current limitations and challenges of using large language models in litigation. We provide insight into the strategies we are enthusiastically exploring to overcome these issues in the development of Ask Blue J. As part of our ongoing research, we discuss some promising ways generative AI can support our work, along with Blue J’s plans to tackle AI’s inherent challenges, furthering our journey into the radical improvement of tax law research with new technology.

Blue J Tax Notes Federal articles:

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