Paul L. Caron

Tuesday, March 21, 2023

Blue J Predicts With 90% Confidence That Taxpayer In Podlucky Would Not Win Innocent Spouse Claim

Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal),  Susan Massey (Director, Blue J Legal) & Christopher Yan (Senior Legal Research Associate, Blue J Legal), Relief of Innocent Spouses — Not So Podlucky, 178 Tax Notes Fed. 1339 (Feb. 27, 2023):

Tax Notes Federal (2022)In this article, the authors use the Blue J machine-learning algorithm to examine Podlucky, an innocent spouse relief case recently decided by the Tax Court, demonstrating how useful artificial intelligence tools can be in calibrating the strengths and weaknesses of clients’ circumstances and predicting likely outcomes in similar cases.

Claims for innocent spouse relief often arise and turn on time and resource-intensive facts and circumstances analyses. Perhaps surprisingly, these kinds of cases are well suited to machine-learning analysis. Practitioners now have access to artificial intelligence tools to assist them in rapidly analyzing likely outcomes for potential claims for innocent spouse relief. These tools can facilitate the careful weighing and calibration of the strengths and weaknesses of the unique circumstances of clients, leading to fully optimized advice and confident settlements. In short, you can be sure that you have truly explored all aspects of your client’s position in a potential settlement when you understand the most likely outcome in court.

To demonstrate, in this article, we examine the facts and circumstances of Podlucky,1 a recent Tax Court case concerning the denial of innocent spouse relief. The pro se taxpayers’ appeal to the Ninth Circuit was dismissed for lack of jurisdiction. We use the facts of this case to illustrate how Blue J’s machine-learning technology could have been used to assess the likelihood of success for an innocent spouse claim. More specifically, we identify several ways in which machine-learning analysis could have led to sharper arguments and better positioning for the taxpayer, based on a deeper exploration of key facts and circumstances. Using machine learning, we determine that the IRS had an overwhelmingly high chance of success had the appeal of the Tax Court’s decision to deny innocent spouse relief not been dismissed, but that there could have been an avenue for the taxpayer to secure at least partial relief with some modest variations of the facts.

Podlucky involves significant underreporting of income and the correlative underpayment of tax on a joint federal income tax return. The IRS claimed that Gregory and Karla Podlucky engaged in fraud and received constructive distributions from a corporate bank account. Karla sought innocent spouse and equitable relief under section 6015(b) and (f), respectively. Blue J’s machine-learning technology predicted with greater than 95 percent confidence that the facts adopted by the Tax Court would not warrant innocent spouse relief. However, if we make favorable inferences about certain underlying facts while applying the apportionment of relief provision set forth in section 6015(b)(2), machine learning indicates that Karla could have increased her chances of receiving partial relief to 59 percent.

We proceed with some background on the facts of the case, the applicable law, and the decision of the Tax Court, before turning to the conclusions we draw from the machine-learning analysis.

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