The Internal Revenue Service said on Monday an initial review of its auditing process confirms the results of a Stanford study showing that Black taxpayers are audited at disproportionately higher rates than other racial groups.
In a letter to Congress dated May 15, IRS Commissioner Danny Werfel pledged to be “laser-focused” on the racial disparities identified in the study and to make needed changes before the next tax filing season.
In late January, a research team co-led by Daniel Ho, the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School and a senior fellow at the Stanford Institute for Economic Policy Research (SIEPR), found that Black taxpayers are 3 to 5 times more likely to be audited than are other taxpayers. The reason, the study authors said, wasn’t overt racism, but problems in the computer algorithms used to spot potential tax cheats.
It also sparked an outcry in Congress. Multiple lawmakers demanded that the IRS investigate the findings, including Senate Finance Committee Chairman Ron Wyden (D-Ore.). Werfel’s letter was a response to Wyden’s request in February for an explanation of the causes of the discrimination and plans for remedying it.
“While there is need for further research, our initial findings support the conclusion that Black taxpayers may be audited at higher rates than would be expected given their share of the population,” Werfel wrote. He said that, while the IRS’s internal investigation into the racial disparities is ongoing, he would stay “laser-focused” on the problem and implement changes prior to the next tax filing season.
In response, Senator Wyden issued a statement praising the study.
“Researchers did a great service spotting the racial bias in the algorithms that guide audit selection,” Wyden said. He vowed to ensure that the IRS “retools these algorithms to eliminate any racial bias.”
In the study, which originated in Ho’s RegLab at Stanford, Ho and his six collaborators show how efforts by the IRS to automate auditing processes ended up unfairly targeting Black taxpayers. The researchers suggest that’s because the IRS set up systems that are more likely to flag tax returns with potential mistakes in how some tax credits, like the earned-income tax credit (EITC), are claimed. There is no evidence that Blacks cheat on their taxes, but they do file at disproportionately higher rates the kinds of returns that the IRS approach targets.
“Dan’s insights into this critical issue — and his leadership of the Stanford team that produced it — epitomize what we strive to achieve at SIEPR: Catalyzing research that leads to better economic policy while simultaneously training the next generation of world-class economic policy scholars,” said Mark Duggan, The Trione Director of SIEPR and The Wayne and Jodi Cooperman Professor of Economics.
Werfel, in his letter to Wyden, noted that the IRS is working to identify racial and other disparities in how it enforces tax compliance. Under the Inflation Reduction Act, the IRS received an additional $80 billion in funding.
“The ongoing evaluation of our EITC audit selection algorithms is the topmost priority within this larger body of work (on disparities in enforcement),” Werfel wrote.
Ho is the faculty director of the Regulation, Evaluation and Governance Lab (RegLab) at Stanford Law School. He is also associate director of the Stanford Institute for Human-Centered AI (HAI).
The study was co-led by Jacob Goldin, a professor at the University of Chicago Law School and former SIEPR faculty fellow. Other authors include: Hadi Elzayn, a research scientist at Meta and former postdoctoral fellow at the RegLab; Evelyn Smith, a doctoral student in economics at the University of Michigan and visiting fellow at the RegLab; Arun Ramesh, a research specialist at the University of Chicago and former research fellow at Stanford Law School; and, Thomas Hertz and Robin Fisher, both of the U.S. Treasury Department’s Office of Tax Analysis.