Relief of Innocent Spouses -- Not so Podlucky

Abstract

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, 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.

Keywords: innocent spouse, tax relief, penalties, machine learning

JEL Classification: H0