| Notably, according to Shykevich, there doesn’t seem to be many guardrails in place yet against eliciting tax scams from ChatGPT— while Open AI developers have trained ChatGPT to identify abusive requests regarding bank information that employ malicious code, Shykevich had no problem prompting schemes from ChatGPT like those noted on the IRS’s Dirty Dozen list. Alarmingly, a list by BeenVerified found that tax and IRS schemes were among the fastest-growing scams of 2022 and increased 55.4 percent compared with the year before. DATA, DATA, DATA: While we didn’t get many details from the new strategic operating plan on exactly how the IRS plans to select high net-worth individuals and large corporations for audit (in large part, according to the agency, because the IRS doesn’t want to give tax cheats helpful clues), we do know that the phrase “analytics” was used a whole lotta times in the report. And given the IRS’s limited resources, it wouldn’t be terribly surprising if the agency is planning to leverage algorithms and computer scientists to manage the audit selection process. That raises all sorts of interesting solutions, but also new potential pitfalls, for the IRS as it tries to capture an estimated half-trillion dollars that escapes the agency’s grasp every year. “So what the IRS is doing, as far as we know any details, is they are having these algorithms look at huge masses of data — and that’s tax return data, financial data, data from third-party reporting and information they’re getting from treaty partners and international information sharing—and they are using that to locate targets,” Rob Kovacev of Miller & Chevalier said. The process could entail, for instance, looking at credits claimed by businesses of a certain size and targeting a business that claimed a credit several standard deviations larger than the average, according to Kovacev. What that might look like: Data analytics are run by the IRS’s in-house research organization, called the office of Research, Applied Analytics, and Statistics (RAAS), which reports directly to Deputy Commissioner for Operations Support Jeff Tribiano. And here’s the intriguing thing: RAAS has been dabbling over the years in some pretty cutting-edge stuff that combines psychology, neuroscience and behavioral economics to improve tax compliance and administration. For instance, in a report published by the RAAS on behavioral insights research, the office cited a study in which the New Mexico state government identified taxpayers who would be most likely to improperly report their unemployment benefits based on the time of day they go online. “Machine learning methods are increasingly used to segment populations based on characteristics, behaviors, or attitudes,” the report said. The dangers: So the IRS could start doing some pretty savvy things once it hires new technologists to write and tweak algorithms for audit selection, but it also comes with the risk that the IRS won’t be able to fully explain how certain taxpayers were selected for scrutiny—as when a Stanford study published earlier this year indicated that Black taxpayers are three to five times more likely than non-Black taxpayers to be audited. If an algorithm wants to maximize collections, it could conclude going after low-income taxpayers is the most efficient way to do so, since those taxpayers don’t have the resources that higher income folks do to fight collections, Kovacev said by way of example. “Because these algorithms are black boxes, this could happen without anyone ever intending that result, or even that it’s happening before it’s already happened,” Kovacev said. WERFEL’S FIRST TEST: Speaking of which, Commissioner Danny Werfel promised to lawmakers at his confirmation hearing that he would get to the bottom of the racial disparities in auditing illuminated by the Stanford study within 60 days, which could prove complicated with all those aforementioned opaque IRS algorithms. In an April 13 letter addressed to Werfel and Treasury Secretary Janet Yellen, Sen. Elizabeth Warren (D-Mass.) reiterated her concern about racial inequities in tax enforcement — noting that 80 percent of the disparity in audit rates between Black and non-Black taxpayers alone can be chalked up to racial inequities within audit rates of Earned Income Tax Credit recipients. The Stanford Study determined that the differences cannot be fully explained by racial differences in income, family size or household structure. “The same report suggests this disparity may be due to IRS audit selection computer algorithms (or assigned scores),” which target under-reporting by taxpayers and overpayments of refundable credits, Warren said.
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