** Caution: External email. Pay attention to suspicious links and attachments. Send suspicious email 

to suspect@treasury.gov ** 

To the Treasury Advisory Committee on Racial Equity, 

Please find attached a new Tax Law Center at NYU Law piece, "New Evidence on Racial

Disparities in IRS Audit Selection Calls for Immediate Action," and please consider this 

a comment to the public meeting on March 9. 

This report reviews and responds to the recent working paper by academic and Treasury 

researchers that found that Black tax filers are 2.9 to 4.7 times as likely to be audited by the IRS 

as non-Black tax filers. The final section of our report, which recommends a series of actions that the 

Biden Administration can take to address audit disparities, in fulfilment of: EO13985; the February 16 

EO addressing algorithmic bias; and IRS Commissioner Nominee Werfel's committment to report on this 

issue in the first 60 days of his taking office. 

Yours sincerely, 

Chye-Ching Huang 

-- 

Chye-Ching Huang

Executive Director

The Tax Law Center at NYU Law

Website | Mailing list

Twitter @TaxLawCenterNYU and @dashching
 

March 2, 2023 

New Evidence on Racial Disparities in IRS Audit Selection Calls for 

Immediate Action 

By: Kathleen Bryant and Chye-Ching Huang 

Summary 

A recent working paper has found that although the Internal Revenue Service (IRS)’s audit selection 

processes are ostensibly “race-blind,” Black tax filers are 2.9 to 4.7 times as likely to be audited by the 

IRS as non-Black tax filers.1 Our report summarizes the paper’s conclusions, explains what we do and 

do not know about the causes of these disparities, and outlines next steps for policymakers to address 

racial disparities in audit selection. 

These steps include modifying the IRS’s confidential audit selection algorithms, reporting publicly and 

regularly on the causes of audit disparities, removing imbalanced reporting requirements that place 

far more attention on refundable tax credits than on much larger sources of tax non-compliance, 

improving the correspondence audit process, and investing in further research. Such actions are 

needed for the Biden Administration to deliver on IRS Commissioner 

Nominee Daniel Werfel’s commitment to release a report on racial audit disparities within his first 60 

days in office, and the Biden Administration’s February 16th Executive Order directing federal agencies 

to “prevent and remedy discrimination, including by protecting the public from algorithmic 

discrimination.”2 

1 Stark Disparities in Audit Rates between Black and Non-Black Tax Filers 

Previously, other researchers have noted that it is likely Black tax filers are audited by the IRS at 

disproportionate rates. For years, public data has shown that households claiming the Earned Income 

Tax Credit (EITC) are audited more frequently than other households, and EITC claimants are 

disproportionately Black (due to a wide range of discriminatory policies and structural barriers that 

have left Black workers over-represented in low-wage jobs).3 But because 

 

3 Margot Crandall-Hollick, “Audits of EITC Returns: By the Numbers,” Congressional Research Service, June 13, 

2022, https://crsreports.congress.gov/product/pdf/IN/IN11952; Chye-Ching Huang and Roderick Taylor, “How 

the 

1 Hadi Elzayn, Evelyn Smith, Thomas Hertz, Arun Ramesh, Robin Fisher, Daniel Ho, and Jacob Goldin, “Measuring 

and Mitigating Racial Disparities in Tax Audits,” Stanford Institute for Economic and Policy Research, January 30, 

2023, https://dho.stanford.edu/wp-content/uploads/IRS_Disparities.pdf.

2 The White House, Executive Order on Further Advancing Racial Equity and Support for Underserved 

Communities Through The Federal Government, February 16, 2023, 

https://www.whitehouse.gov/briefingroom/presidentialactions/2023/02/16/executive-order-on-furtheradvancing-racial-equity-and-support-forunderservedcommunities-through-the-federal-government/. While this 

brief focuses specifically on audit-related recommendations, as Steven Dean and others have noted, addressing 

racial disparities in IRS audit processes are not the only steps needed to advance racial equity in the tax system 

See: Lauren Loricchio, “Werfel’s Promised Report on Racial Disparities Seen as First Step,” Tax Notes, February 

23, 2023, https://www.taxnotes.com/taxnotes-todayfederal/tax-system-administration/werfels-promisedreport-racial-disparities-seen-firststep/2023/02/23/.
Federal Tax Code Can Better Advance Racial Equity,” Center on Budget and Policy Priorities, July 25, 

2019, 

 

 

federal income tax returns do not collect information on the race and ethnicity of tax filers, there has 

been no direct evidence on whether Black filers are more likely to face audits than non-Black filers. 

The new working paper by Hadi Elzayn (Stanford University), Evelyn Smith (University of 

Michigan), Thomas Hertz (U.S. Treasury Department), Arun Ramesh (University of Chicago), 

Robin Fisher (U.S. Treasury Department), Daniel Ho (Stanford University), and Jacob Goldin (University 

of Chicago and U.S. Treasury Department) estimates the likely racial identity of tax filers based on 

their first names, last names, and home addresses listed on tax returns, and then compares audit 

rates for Black and non-Black tax filers. 

Elzayn, Smith, et al. find that audit disparities between EITC claimants and those without EITC claims 

explain only about a fifth of the overall audit disparity between Black and non-Black tax filers. Instead, 

nearly 80% of the overall disparity is driven by differences within the population of EITC claimants 

selected for audits. Black EITC claimants are 2.9 to 4.4 times as likely to be audited as non-Black EITC 

claimants. 

Black EITC claimants remain more likely than non-Black EITC claimants to be audited even among 

families of the same household composition, family size, and income. For example, among unmarried 

men that claim children for the EITC, Black tax filers are more than twice as likely to be audited as 

nonBlack tax filers. 

Furthermore, audit rate disparities cannot be fully explained by “true” differences in tax 

underreporting between Black and non-Black tax filers claiming the EITC. The researchers sorted tax 

filers into ten equally sized groups based on the dollars of under-reported tax ultimately identified 

during audits, and they found that within each group, Black tax filers were more likely to be selected 

for audits than nonBlack tax filers. 

The paper notes that racial disparities in audit selection could compound financial insecurity that Black 

tax filers are already more likely to experience than non-Black filers because of barriers to 

employment and wealth-building erected by public policy and private discrimination. For example, the 

working paper states that tax audits “can exacerbate financial strain for the lowest income taxpayers 

– whose tax refunds are typically frozen while an audit is in place”;3there is suggestive evidence that 

audits can deny tax benefits to filers who are truly eligible simply because they cannot make it 

through a burdensome audit process;4 and prior research shows that 

 

https://www.cbpp.org/research/federal-tax/how-the-federal-tax-code-can-better-advance-racial-equity; Paul 

Kiel and Hannah Fresques, “Where in The U.S. Are You Most Likely to Be Audited by the IRS?”, ProPublica, April 1, 

2019, https://projects.propublica.org/graphics/eitc-audit. 

3 Elzayn, Smith, et al., supra note 1. 

4 Kathleen Bryant, Chye-Ching Huang, Leslie Book, T. Keith Fogg , and Nina E. Olson, “Exclusionary Effects of 

the IRS Correspondence Audit Process Warrant Further Study,” The Tax Law Center at NYU Law and the Center 

for Taxpayer Rights, February 18, 2022, 

https://www.law.nyu.edu/sites/default/files/TLC%20CTR%20Memo%20on%20Need%20for%20EITC%20Audit

% 20Study_TLC%20Site.pdf.
the experience of being audited discourages tax filers from claiming the EITC in future years even 

when they are likely eligible for the credit.5 

2 Why Is This Happening? An Overview of Four Possible Factors 

The IRS selects tax returns for audits using several confidential processes, including by using 

algorithms it has designed to identify potential tax non-compliance. In this context, an “algorithm” is 

simply a series of calculations that follow a set of rules, the results of which the IRS uses to decide (or 

help decide) which filers to audit. For example, about half6 of tax returns audited by the IRS’s Small 

Business and Self-Employed Division in 2018 were selected using a model that assigns “scores” based 

on the likelihood of identifying tax under-reporting. The rules for calculating that score are an 

“algorithm.”8 

But the IRS keeps private the full details of how its audit selection algorithms and other processes 

work – otherwise, the most unscrupulous would-be tax evaders might use that information to avoid 

being selected for audit. And it is not clear what exact share of audits the IRS selects exclusively or 

partially using an algorithm. 

Indeed, the IRS did not even give the authors of the paper full information about its audit selection 

algorithms and processes. This means that the paper cannot fully explain what features of those 

algorithms – and other aspects of tax administration – drive the racial disparities that the paper 

uncovered. 

Instead, Elzayn, Smith, et al. create some illustrative audit algorithms. These illustrations give 

important clues in four main areas about what could be driving racial disparities in IRS audit 

algorithms and other processes. Before discussing each in more detail, we outline them here: 

a. The choice of “targets” for audit selection algorithms. We do not know whether the IRS’s 

audit algorithms aim to select filers who are most likely to owe any dollars of under-reported 

tax; concentrate audits on filers likely to owe the largest dollar amounts of tax; target audit 

resources to those with the highest “return on investment” or ROI (i.e., 

 

dollars recovered relative to costs per audit); or target some other criteria. However, the 

paper shows that, in general, algorithms targeting the presence of tax under-reporting 

5 Dayanand Manoli, John Guyton, Kara Leibel, Ankur Patel, Mark Payne, and Brenda Schafer, “The Effects of 

EITC Correspondence Audits on Low-Income Earners,” June 15, 2022, 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4137352.

6 Treasury Inspector General for Tax Administration, “Individual Returns with Large Business Losses and No 

Income Pose Significant Compliance Risk,” Report No. 2020-30-056, September 28, 2020, 

https://www.tigta.gov/reports/audit/individual-returns-large-business-losses-and-no-incomeposesignificantcompliance.

8 The IRS’s Discriminant Function System (DIF) is a “supervised machine learning” 

algorithm. Supervised machine learning algorithms use historical data (where an outcome of interest is known) to 

“train” a model to identify that outcome in future data, based on the characteristics most associated with that 

outcome in the historical dataset. In this case, the DIF uses audit data from previous years to identify which 

characteristics from tax returns are most closely associated with tax under-reporting and uses those characteristics 

to assign a DIF score to each return. In contrast, a conventional algorithm would require the IRS to specify up 

front the set of rules used to select tax returns for audit (for example, based on characteristics that independent 

research has identified as key drivers of tax noncompliance). For more on the DIF algorithm, see: Getaneh 

Yismaw, Drew 

Johns, Taukir Hussain, Jonathan Creem, and Mary-Helen Risler, “Enhancing Return Risk Assessment for 

Examination: Recent DIF (Discriminant Function) Model Updates,” IRS Research Bulletin, June 24, 2021,

https://www.irs.gov/pub/irs-pdf/p1500.pdf#page=161. 
disproportionately audit Black filers while algorithms targeting the magnitude of tax 

underreporting audit Black filers at lower rates. Furthermore, algorithms that specifically 

target overclaims of refundable tax credits, rather than tax under-reporting in general, 

produce significant racial audit disparities. 

b. Tax filer characteristics used for audit selection. Once an audit algorithm is given a target 

(say, the likelihood of having any tax under-reporting), it will select tax returns for audits 

based on the characteristics of tax filers that are most closely associated with the measure of 

tax noncompliance that is being targeted. Because race is not directly reported on tax returns 

or in other administrative data that the IRS uses, it cannot be one of the characteristics 

directly used to identify tax non-compliance or to select returns for audit. However, certain 

characteristics may nevertheless be highly correlated with race and drive racial audit 

disparities – we explain further below. 

c. IRS funding constraints. External constraints, such as lack of funding for more complex 

audits, can contribute to racial disparities in audit selection. The Inflation Reduction Act’s $80 

billion investment can help narrow racial audit disparities by reducing the share of all audits 

that target EITC claimants (who drive most of the overall disparity) and increasing audits on 

highincome tax filers (who are disproportionately non-Black).7 Budget constraints also likely 

contribute to audit disparities within the population of EITC claimants. EITC claimants with 

business income are more likely to be non-Black but are less likely to be audited than EITC 

claimants without business income, potentially because audits involving business income are 

more expensive to administer. 

d. Burdensome audit processes with racially disparate impacts leading to a “doom loop.” We 

have previously explained that a potentially significant share of EITC recipients are unable to 

navigate the IRS’s complex audit processes and have their claims denied by default, despite 

meeting the credit’s eligibility criteria.8 The administrative burdens of IRS audit processes 

likely fall heaviest on tax filers with limited access to professional representation and other 

barriers to navigating the audit process. And these characteristics could potentially be 

correlated with race, because of both “historical racism and contemporary patterns of racial 

discrimination and bias.”9

Algorithms that “learn” from historical audit outcomes which tax filers are least likely to 

successfully navigate burdensome audit processes may compound racial audit disparities over 

time, by disproportionately selecting those with the steepest barriers to defending 

themselves during an IRS audit. 

 

Below, we review each of these four factors in more detail and explain what the working paper does 

and does not tell us about each of them. Key questions remain unanswered, so lawmakers should take 

7

Julie-Anne Cronin, Portia DeFilippes, and Robin Fisher, “Tax Expenditures by Race and Hispanic Ethnicity: An 

Application of the U.S. Treasury Department's Race and Hispanic Ethnicity Imputation,” U.S. Treasury 

Department, Office of Tax Analysis, January 2023, https://home.treasury.gov/system/files/131/WP122.pdf#page=35. 

8 Bryant et al., supra note 5. 

9 Huang and Taylor, supra note 3. 
immediate action to address racial audit disparities and to understand more completely what is 

causing them. 

2.1 The Choice of “Targets” for Audit Selection Algorithms 

Elzayn, Smith, et al. were not given full information on the IRS’s audit selection processes and 

algorithms for their study. Instead, they built several illustrative audit selection models10 that 

demonstrate how algorithmic design choices can either exacerbate or reduce racial disparities in audit 

selection.11 

For example, they compared the racial disparities created by two illustrative models given different 

“targets:”12 one that selected tax returns based on the likelihood that they had any under-reported 

tax above $100 and another that selected returns with the highest dollar amounts of under-reported 

tax. For example, say these models estimate, based on information that the IRS has and based on past 

audit data results, that filer A is likely to owe $100, and filer B is equally likely to owe $1,000. The first 

illustrative audit algorithm will rank these filers equally for audit selection, while the second 

illustrative algorithm will rank B for selection over filer A. 

The model targeting the magnitude of underreporting not only collected more revenue than the 

alternative model, but also audited Black EITC claimants at lower rates than non-Black EITC claimants. 

In contrast, the model predicting any dollars of under-reporting audited Black EITC claimants at 

significantly higher rates than their non-Black counterparts. 

As one of the paper’s authors, Evelyn Smith, explained: “the choice to focus on whether there is 

underreporting, as opposed to the magnitude of underreporting, is connected to broader structural 

sources of economic inequality and racial justice.”13 Black tax filers have lower incomes on average 

than non-Black tax filers, because of centuries of racist policies and systems that have created 

structural barriers to economic opportunity.14 As a result, Black tax filers generally have less income to 

misreport on their tax returns. This means that “focusing audits on the amount of 

 

10 The paper refers to these as “counterfactual” models. 

11 These models were “trained” to predict under-reporting among EITC claimants based on audit data from the 

IRS’s National Research Program (NRP). Unlike the operational audits the IRS conducts for day-to-day compliance 

purposes, NRP audits are conducted on a “stratified random sample” of tax filers within different income and other 

categories. That is, the purpose of NRP audits is to produce information about non-compliance within different tax 

filing populations, rather than seeking to only audit those most likely to be non-compliant. 

12 The study refers to this as the “prediction model objective.” 

13 Katharine Miller, “IRS Disproportionately Audits Black Taxpayers,” Stanford University Human-Centered 

Artificial Intelligence,” January 31, 2023, https://hai.stanford.edu/news/irs-disproportionately-auditsblacktaxpayers. 

14 Huang and Taylor, supra note 3. 
underreported income will disproportionately end up focusing on higher income individuals who are 

15less likely to be Black taxpayers.”13

 

Actual IRS algorithms and audit processes may not explicitly aim to subject a filer owing an estimated 

$100 to an equal chance of being audited as a filer estimated to owe $1,000.16 But other choices in 

what algorithms are targeting could, in less obvious ways, prioritize selecting tax filers with any dollars 

of tax under-reporting rather than higher dollar amounts of underreporting.17 

For example, IRS algorithms might target some measure of the expected “return on investment” (ROI) 

for audits, taking into account not only the tax dollars recovered, but also the costs of conducting 

audits. As discussed further in section 2.3, audits of filers likely to owe higher dollar amounts of 

underreported tax may be systematically more expensive to conduct (relative to the amount of tax 

likely to be owed) than audits of returns likely to generate lower amounts.18 Black tax filers in general, 

and Black EITC claimants specifically, are also less likely to have items on their tax returns (such as 

substantial amounts of “business income”) that are more resourceintensive to audit. As a result, 

algorithms with ROI-based targets could be producing racial disparities in audit selection by 

systematically selecting tax filers that are the “cheapest” to audit, all else equal, even if those tax filers 

are responsible for fewer dollars of tax under-reporting on average. 

As discussed in more detail in section 2.4, this could be especially problematic if the costs of 

conducting audits, and audit outcomes, bear no relationship to underlying tax non-compliance, but 

simply reflect the extent to which some tax filers face greater barriers to navigating burdensome IRS 

audit processes than others. It might be “cheaper” to audit tax filers that the IRS considers least likely 

to successfully challenge any tax under-reporting identified. And if the barriers to completing the IRS 

audit process tend to fall heaviest on Black tax filers, they could be audited at higher rates for reasons 

that have nothing to do with underlying tax compliance. 

There are other ways that the choice of “targets” for audit selection algorithms can create racial 

disparities. Elzayn, Smith, et al. created another illustrative model designed to specifically identify 

overclaiming of refundable tax credits (including the EITC, Child Tax Credit, and American Opportunity 

Tax Credit), instead of tax under-reporting for any reason. This audit selection model recovered fewer 

total dollars of under-reported tax and created larger audit disparities between Black and non-Black 

EITC claimants than the other models tested. 

The researchers designed this illustrative model because refundable tax credits are scrutinized 

disproportionately under reporting for “improper payments,” and this imbalance could be reflected in 

the IRS’s audit selection criteria. Under the Office of Management and Budget (OMB)’s inappropriate 

interpretation of the Payment Integrity Information Act (PIIA), the IRS and Treasury are required to 

report more frequently and in more detail on refundable tax credit error than on other sources of tax 

non-compliance that contribute more to the “tax gap” (the gap between federal taxes paid and taxes 

owed).17 

15 Miller, supra note 15. 

16 If they are also estimated to have the same likelihood of owing each of those amounts respectively. 

17 Indeed, there is a full spectrum of algorithms lying between the two illustrated targets. 

18 Former IRS Commissioner Rettig has stated that audits on EITC recipients are the “most efficient use of available 

IRS examination resources.” See: Charles P. Rettig, Letter to Sen. Ron Wyden, September 6, 2019, 

https://s3.documentcloud.org/documents/6430680/Document-2019-9-6-Treasury-Letter-to-Wyden-RE.pdf.

17

The Treasury Department estimates that the highest-income 1% of tax filers alone are responsible for about 28% 

($163 billion) of the gap between federal taxes paid and taxes owed each year, while the IRS estimates that EITC 
Again, we do not know what targets are used in the IRS’s actual audit selection algorithms, and to 

what extent unbalanced systems like the “Improper Payments” regime factor into how the IRS designs 

algorithms. But the working paper clearly shows that algorithmic design choices, such as the measures 

of tax non-compliance used and the types of non-compliance prioritized, can drive substantial racial 

disparities in audit selection. 

2.2 Tax Filer Characteristics Used for Audit Selection 

Once a “target” has been chosen, audit selection algorithms identify which filers are most likely to 

have tax under-reporting based on certain characteristics on their tax returns and other sources of IRS 

administrative data. Importantly, if IRS audit selection algorithms work like most other “machine 

learning” algorithms, the characteristics used to select tax filers for audits may not directly cause tax 

non-compliance – they only need to be correlated with tax non-compliance for the IRS to prioritize 

them in the audit selection process. 

There is some information publicly available about the types of characteristics that the IRS’s 

algorithms use to identify tax under-reporting. For example, many EITC returns are automatically 

flagged for audits under the IRS’s Dependent Database Program, which checks information about 

dependents claimed on tax returns against other internal IRS data and administrative data from other 

federal agencies like the Social Security Administration. But the exact inputs underlying the IRS’s audit 

selection algorithms, and which characteristics are given the heaviest weight when selecting tax filers 

for audit, are unknown. In this study, Elzayn, Smith, et al. trained their illustrative audit algorithms to 

identify tax underreporting based on characteristics that available evidence suggests are used in the 

IRS’s actual algorithms, including information on income reported on tax returns, deductions claimed, 

and flags for any incorrectly claimed dependents. 

Because race is not reported on tax returns or in other IRS administrative data, it cannot be one of the 

characteristics directly used to identify tax non-compliance and select returns for audit. However, 

certain characteristics may nevertheless be highly associated with race and drive racial audit 

disparities. For example, Smith noted in a recent interview that Black tax filers may be 

 

more likely to have errors related to claiming dependents on their tax returns because of overly 

complex eligibility criteria for the EITC that are insufficiently inclusive.18 

However, as described above, differences between Black and non-Black tax filers in household 

structure do not fully explain audit disparities.19 Because Elzayn, Smith, et al. did not have access to 

the details of IRS algorithms, the study could not answer the following key questions: 

recipients are responsible for just 6% ($28 billion) of it. See: Natasha Sarin, “The Case for a Robust Attack on 

the Tax Gap,” U.S. Treasury Department, September 7, 2021, https://home.treasury.gov/news/featuredstories/thecasefor-a-robust-attack-on-the-tax-gap; Internal Revenue Service, “Federal Tax Compliance Research: 

Tax Gap Estimates for Tax Years 2014–2016,” August 2022, https://www.irs.gov/pub/irs-pdf/p1415.pdf. 

18

Steve Inskeep, “Does the IRS audit some people more often than others?”, NPR, February 1, 2023, 

https://www.npr.org/2023/02/01/1153150854/does-the-irs-audit-some-people-more-often-than-others. See also: 

Katherine M. Michelmore and Natasha V. Pilkauskas, “The Earned Income Tax Credit, Family Complexity, and 

Children’s Living Arrangements,” The Russell Sage Foundation Journal of the Social Sciences, Vol. 8 No. 5, 

2022, pp. 143–65, https://www.rsfjournal.org/content/8/5/143.

● What characteristics used to identify potential tax under-reporting in the IRS’s audit selection 

algorithms are most associated with race? 

19 It is unclear what exact share of the disparity between Black and non-Black EITC filers can be explained by the 

combination of differences in household composition, income, and other factors that the paper examines, because 

the paper looks at each of these factors individually but does not explicitly show how the combination of these 
● Which of these characteristics contribute most to audit selection disparities?20

 

For instance, we do not currently know whether factors such as claiming the EITC or filing a tax return 

for the first time increase the chance of selection for audit. And we do not know how strongly such 

factors are associated with race. If such factors are used in audit selection, and they are associated 

with race, they could simply be reflecting unequal barriers to accessing reliable guidance and 

assistance for filing tax returns and claiming tax benefits. Policymakers should respond by removing 

those barriers rather than letting them determine downstream compliance and audit activity in a way 

that compounds racial inequities. 

The study emphasizes the importance of understanding what factors are used by IRS algorithms to 

select filers for audits, and which of these factors introduce racial disparities into audit selection. 

2.3 IRS Funding Constraints 

Between 2010 and 2021, the IRS’s overall budget was cut by about 20% and funding for IRS 

enforcement activities by about 23%, after adjusting for inflation.21 These budget constraints have 

caused audit rates on higher-income filers and large corporations to plummet faster over the past 

decade than audits on returns with EITC claims, because the latter are much less expensive to 

conduct. 

 

The Inflation Reduction Act’s $80 billion investment in the IRS will allow the agency to shift tax 

enforcement focus away from low-income filers and towards higher-income tax filers and 

corporations. Smooth implementation of that funding can potentially help narrow racial audit 

disparities. Even though this paper found that racial audit disparities are driven primarily by 

differences within the population of EITC claimants (as described above), a fifth of the current top-line 

disparity is driven by disparities between EITC and non-EITC filers. Using the $80 billion for its 

intended purpose of increasing compliance among (and, accordingly, audits on) high-income filers 

(who are disproportionately nonBlack)22 will mechanically reduce the factor by which audit rates for 

Black tax filers exceed those for non-Black tax filers. 

Further, the paper shows that IRS budget constraints can also affect the allocation of audits within the 

population of EITC filers. Only about 7% of EITC claimants under audit have “substantial business 

income,” even though these tax filers may be responsible for a disproportionate share of tax 

underreporting among EITC recipients. The IRS may conduct relatively fewer audits on EITC claimants 

with business income because they cost nearly $400 per audit to complete, compared to about $30 

per audit 

factors affects racial disparities. The paper displays audit rates by race within key subgroups of interest, such as 

single men with dependents in Figure 6. See Elzayn, Smith, et. al, supra note 1. 

20 This will be determined based on a combination of how associated a characteristic is with race, and how much 

weight is placed on that characteristic when selecting tax filers for audit. 

21 Chuck Marr, Samantha Jacoby, and Jabari Cook, “Success of the IRS Rebuilding and Tax Gap Reduction Effort 

Depends on Sufficient Funding Through Annual Appropriations,” Center on Budget and Policy Priorities, 

December 

7, 2022, https://www.cbpp.org/research/federal-tax/success-of-the-irs-rebuilding-and-tax-gapreductioneffortdepends-on. 

22 Cronin, DeFilippes, and Fisher, supra note 9. 
for EITC claimants without business income.23 Non-Black EITC claimants are also more likely to have 

substantial business income on their returns than Black EITC claimants. 

Elzayn, Smith, et al. illustrate how resource limitations can exacerbate racial audit disparities among 

EITC claimants. They imposed a constraint on each counterfactual model that they created, requiring 

the models to select the “status quo ratio of business to non-business EITC returns for audit.” For each 

model tested, this constraint reduced the total dollars of tax underreporting identified and increased 

the share of Black tax filers selected for audits. Yet again, we do not know whether such constraints 

are incorporated in the IRS’s confidential audit algorithms, but it is clear that funding limitations can 

drive racial disparities in audit selection. 

2.4 Administrative Burden “Doom Loop” 

While the IRS conducts several types of audits, the vast majority of EITC claimants are audited via 

correspondence examination (by mail), rather than by office audit (in-person at an IRS location), or 

field audit (in-person at the tax filer’s home). Correspondence audits are significantly cheaper for the 

IRS to conduct because they take significantly less time to close – on average, only two hours per 

audit, compared to 34 hours per in-person audit.2425 As EITC claimants are disproportionately Black, it 

is therefore unsurprising that Elzayn, Smith, et al. found that a higher share of Black filers is selected 

for correspondence audits than non-Black filers. In contrast, Black and non-Black tax filers are selected 

for field audits and office audits at roughly similar rates. 

 

With the Center for Taxpayer Rights, we have written previously about the substantial administrative 

burdens that the correspondence audit process imposes on low-income tax filers claiming the EITC.25

IRS letters and forms are difficult to understand, documents verifying eligibility can be onerous to 

track down, and IRS phone lines are often too swamped with calls for tax filers to reach customer 

service representatives. For these reasons, the majority of EITC claimants undergoing correspondence 

audits either do not respond at all or respond insufficiently to IRS inquiries and have the credit denied 

by default.26 

These administrative burdens are deeply tied to discrimination and policy barriers to full economic 

and social inclusion that are inequitably distributed by race. As an OMB report implementing the 

Biden Administration’s Executive Order on Advancing Racial Equity has noted, administrative burdens 

“do not fall equally on all entities and individuals, leading to disproportionate underutilization of 

critical services and programs, as well as unequal costs of access, often by the people and 

communities who need them the most.”27 For example, if hypothetically, Black tax filers are less likely 

to have access to a Low Income Taxpayer Clinic (LITC) or a Taxpayer Assistance Center (TAC) where 

they could seek in-person assistance, they may have more difficulty navigating the audit process. If 

the administrative burdens of 

23 Miller, supra note 15. 

24 Janet Holtzblatt, “The Effect of Tax Enforcement on Revenues,” Testimony Before the Select Revenue Measures 

Subcommittee and Oversight Subcommittee, Ways and Means Committee, United States House of Representatives, 

June 10, 2021, https://www.urban.org/sites/default/files/publication/104363/the-effect-of-taxenforcementonrevenues_v2.pdf. 

25 Bryant et al., supra note 5. 

26 Manoli et al., supra note 6. 

27 Shalanda D. Young, “Study to Identify Methods to Assess Equity: Report to the President,” Office of 

Management and Budget, July 2021, https://www.whitehouse.gov/wp-content/uploads/2021/08/OMBReportonE013985-Implementation_508-Compliant-Secure-v1.1.pdf. 
IRS audit processes fall heaviest on Black tax filers, they could be more likely to lose access to the EITC 

and other tax benefits during audits than non-Black tax filers. 

Elzayn, Smith, et al. did not explicitly explore this possibility in their working paper, but if the IRS uses 

historical data from audit outcomes to predict non-compliance and select tax returns for audit, their 

algorithms could create a “doom loop.”28 Tax filers that face the greatest barriers to overcoming 

administrative burdens during the correspondence audit process may be disproportionately Black or 

other people of color. As a result, these tax filers may be more likely to owe tax dollars on audit, due 

to non-response or partial response to the audit process, rather than because of any difference in the 

likelihood that their original return was in fact noncompliant. Over time, the IRS’s algorithms may be 

“learning” from previous audit outcomes to disproportionately select Black tax filers for audits.29

Rather than reflecting the “true” accuracy of returns, this means filers who have the greatest difficulty 

making it through complex audit processes keep being selected.30 

Other audit selection choices and algorithm rules could have similar impacts. For instance, the IRS’s 

Dependent Database Program is calibrated (at least in part) to minimize the “no-change rate” (the 

probability that an audit ends with no change in taxes owed). This could have a similar impact of 

concentrating audit selection on those who face the greatest barriers to making it through an audit 

without a change to an initial return rather than on those who are most noncompliant, potentially 

with racially disparate impacts. 

Likewise, to the extent that audit algorithms include some measure of ROI, this could end up 

intensifying audit risks for those who face a correspondence audit but do not respond to it at all –

likely one of the cheapest audit outcomes for the IRS to administer. ROI could be relatively high for 

such returns even if the amounts owed are small. We do not know whether non-response rates are 

higher for Black than non-Black filers due to higher administrative burdens, but if so, this dynamic 

could be driving racial disparities in audit selection. 

3 What Policymakers Should Do Next 

The working paper provides strong evidence of large racial disparities in audit selection and illustrates 

how audit selection algorithms can have disparate impacts by race even when they do not explicitly 

use 

28 Negative feedback loops are a topic of concern in the algorithmic fairness literature more broadly. See, for 

example: Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramani, 

“Runaway Feedback Loops in Predictive Policing,” Proceedings of Machine Learning Research, Vol. 81, 2018, pp. 

1-12, http://proceedings.mlr.press/v81/ensign18a/ensign18a.pdf. For more generally on algorithmic bias and related 

practices that produce discriminatory outcomes for Black communities, see: https://jointcenter.org/tag/data-privacy/.

29 This pattern could emerge on net even if the algorithm does not increase audit selection risk for specific 

filers who have been audited and owed any amounts in the past. However, such selection criteria would create an 

even more direct feedback loop that could compound racial disparities in audit selection. Again, we do not know 

whether IRS audit algorithms work in this way. 

30 Even algorithms “trained” using NRP data, rather than operational audit data, risk creating a “doom loop.” 

NRP audits generally involve more extensive personal contact between IRS examiners and tax filers, so they have 

higher response rates than operational audits. However, the NRP audit process can still be “burdensome and 

intensive” for tax filers and data from 2006-2008 found that the NRP still has a significant non-response rate of 

about 15% for 

EITC filers. See: National Taxpayer Advocate, “Compensate Taxpayers for “No Change” NRP Audits and Waive 

Assessment of Tax, Interest, and Penalties Resulting from NRP Audits,” 2017 Annual Report to Congress, 

https://www.taxpayeradvocate.irs.gov/wp-content/uploads/2020/08/ARC17_Volume1_LR_08_NRP.pdf; Rosemary 

Marcuss, Alain Dubois, Janice Hedemann, Mary-Helen Risler, and Kara Leibel, “Compliance Estimates for the 

Earned Income Tax Credit Claimed on 2006-2008 Returns,” Internal Revenue Service Research, Analysis & 

Statistics Report Publication 5162, August 2014, 

https://www.irs.gov/pub/irssoi/EITCComplianceStudyTY20062008.pdf. 
race. The Biden Administration can take immediate steps to increase the understanding of what 

causes racial audit disparities and make progress on reducing them: 

a. Modify audit algorithms. Important lessons learned from this paper can be used immediately 

to examine and modify confidential IRS audit selection models. For example, the paper makes 

clear that prioritizing tax filers for audits based on the magnitude of predicted tax 

underreporting, rather than the mere presence of tax underreporting can reduce racial audit 

disparities. It is also clear that targeting overpayments of refundable tax credits during audits, 

rather than other types of tax under-reporting, can exacerbate disparities. Such practices should 

be immediately addressed. 

b. Report publicly and regularly on audit disparities. 

i. Report within 60 days. In his confirmation hearing for the role of IRS Commissioner, 

Daniel Werfel committed to releasing a report on the audit disparities documented 

between Black and non-Black tax filers within his first 60 

 

days in office.28 This report should clearly identify and explain what elements of audit 

selection algorithms are causing these disparities and describe the steps the IRS will 

take to address them. It should also include commitments to report further and 

regularly on these issues in the years to come. This report should address specific 

questions including: 

1. What are the “targets” chosen for various IRS audit selection algorithms, and 

how do those targets affect racial audit disparities? 

A. To what extent do IRS algorithms or other parts of the audit selection 

process prioritize selecting returns for audits that are likely to owe 

any dollars of under-reported tax, rather than the largest dollar 

amounts of under-reported tax? 

B. Do funding limits cause the IRS to constrain their audit selection 

models to prioritize the types of audits that are cheaper to conduct 

(such as correspondence audits) either directly or by including some 

calculation of “ROI” that places higher weight on auditing filers who 

are least likely to respond to an audit request? If so, to what extent 

do these constraints contribute to racial audit disparities? 

C. Do IRS audit processes prioritize selecting tax filers for audit who are 

more likely to have made certain types of error (such as EITC and CTC 

error) relative to other errors, even when the other errors contribute 

the same or more to tax underreporting? 

2. What characteristics used to identify potential tax under-reporting in the IRS’s 

audit selection algorithms are most associated with race, and which 

contribute 

28 Naomi Jagoda and Chris Cioffi, “IRS Nominee Werfel Pledges Transparency with $80 Billion,” Bloomberg Tax, 

February 15, 2023, https://news.bloombergtax.com/daily-tax-report/senate-finance-holds-confirmationhearingforirs-nominee-werfel. 
most to disparities in audit selection? To what extent do these factors simply 

magnify barriers to tax filing that are unequally distributed by race? 

The IRS may be reluctant to publicly disclose the full details behind their 

algorithms, to prevent any would-be tax evaders from gaming the audit 

selection process. But this should not stop the IRS from disclosing the specific 

characteristics in its algorithms that are acting as proxies for race and 

introducing bias into the audit selection process; such factors should be 

subjected to especially intense scrutiny and accountability. It is also 

insufficient to simply remove these characteristics from IRS algorithms 

without publicly disclosing them. To prevent racial bias from re-emerging in 

the future, questionable components of IRS algorithms should be 

 subject to significant oversight and research scrutiny so that it is 

clear how they introduced such bias into the audit selection process in the 

first place. 

3. How do administrative burdens in IRS audits contribute to disparities in audit 

selection and outcomes? 

A. Are the administrative burdens of the correspondence audit process 

inequitably distributed by race? 

B. To what extent are IRS algorithms “learning” over time to select tax 

filers with the greatest barriers to navigating audits and thereby 

reinforcing racial disparities in audit selection? 

If any of these questions cannot be answered within 60 days, the report should detail 

what steps the IRS is taking to respond and when these questions will be answered. 

As already noted, the Administration should not delay in taking immediate action to 

reduce audit disparities, even if further research is needed to better understand the 

full range of factors driving them. 

ii. Annual or regular disclosures. The Administration can commit to annually or 

periodically estimating and disclosing disparities in rates of audit selection between 

Black to non-Black tax filers, including controlling for factors such as income and 

household composition. Regular reporting could also help to ensure that future 

changes to tax administration (such as adopting further use of machine learning 

algorithms as some have suggested) are implemented in ways that do not increase 

racial disparities in audit outcomes. 

iii. Extension of analysis to other communities. As part of the 60-day report or 

subsequent reporting, the IRS should address the feasibility of extending this type of 

analysis to other racial and ethnic groups, including Latino, Asian American and Pacific 

Islander, and Native American/Alaska Native tax filers. Other recent Treasury 

research29 that uses similar methods for imputing the race and ethnicity of tax filers 

shows that it can be difficult to generate reliable estimates for certain communities 

with existing data 

29 Cronin, DeFilippes, and Fisher, supra note 9. 
sources. If this is the case, the report should clearly describe these methodological 

challenges – and what approaches could help overcome them. Further research could 

also aim to disaggregate audit rates along other lines of difference. 

If the Administration does not proactively provide the information detailed above or commit 

to providing it, lawmakers and other relevant oversight bodies should ensure that they do so. 

c. Remove refundable tax credits from “improper payments” reporting and bolster 

“tax gap” reporting. The IRS should not be subject to skewed requirements to focus 

 

more on refundable tax credit error than on other sources of tax non-compliance that 

contribute more to the tax gap. Accordingly, OMB should remove the EITC and other 

refundable tax credits from the PIIA’s reporting regime, which the Administration has 

incorrectly interpreted to require the IRS and Treasury to report more frequently and in more 

detail on “improper payments” of refundable tax credits such as the EITC and CTC than on 

sources of tax non-compliance among high-income tax filers and businesses. These 

imbalanced reporting requirements reflect long-standing racial and class biases around which 

groups should be investigated and punished for “fraud” by law enforcement authorities, 

versus which groups should not be because they are not deemed 

“criminally suspicious.”33 For more, see our issue brief.34 

d. Make the correspondence audit process more accessible and equitable. The Administration 

should explore options for reducing the administrative burdens in the correspondence audit 

process, which are potentially borne disproportionately by Black and other marginalized tax

filers and may be feeding racial disparities in audit selection. As we have previously 

recommended, the IRS could conduct or facilitate research evaluating whether certain 

interventions can boost participation in correspondence audits, such as initiating repeated 

contact attempts or assigning personalized assistance to tax filers selected for audits. For more, 

see our previous report co-authored with the Center for Taxpayer Rights.35 

e. Identify and remove inequitable barriers to reliable tax preparation services and guidance. 

Our report has focused primarily on IRS algorithms, but other elements of tax administration 

may have disparate impacts by race that feed racial disparities in audit selection. Evidence 

suggests that most EITC errors are due to honest mistakes, not intentional fraud,36 and tax 

filers 

33 See: Dorothy A. Brown, “Race and Class Matters in Tax Policy,” Columbia Law Review, Vol. 107 No. 3, April 

2007, pp. 790-831, https://www.jstor.org/stable/40041719; Sam Washington, “Race, Tax Enforcement and the 

Social Compact,” Kalmanovitz Initiative for Labor and the Working Poor, Georgetown 

University, https://lwp.georgetown.edu/news/race-tax-enforcement-and-the-social-compact/. 

34

The Tax Law Center, “Rebalancing Reporting on Sources of the Tax Gap,” May 3, 2022, 

https://www.law.nyu.edu/sites/default/files/Improper%20Payments%20Refundables%20vs.%20Other%20Tax%20

G ap%20Sources.pdf.

35 Bryant et al., supra note 5. 

36 Robert Greenstein, John Wancheck, and Chuck Marr, “Reducing Overpayments in the Earned Income Tax 

Credit,” Center on Budget and Policy Priorities, January 31, 2019, 

https://www.cbpp.org/research/federaltax/reducing-overpayments-in-the-earned-income-tax-credit. 

may not have equal access to guidance and services that help them file their tax returns 

accurately. For example, factors such as where the IRS does and does not offer in-person 
assistance for tax filing and settling disputes; lawmakers’ failure to require certain tax 

preparers to meet minimum standards for competency;30 and unequal access to plainlanguage tax guidance, online tools, and 

 

telephone assistance may all have impacts that differ by race, geography, and other 

dimensions of difference. Policymakers should seek to identify and remove barriers to 

accessing tax guidance and reliable tax preparation services, rather than allowing their effects 

to ripple through to choices about how to conduct compliance and audit activity. The 

Administration has already announced some efforts to expand access to tax filing assistance, 

and these efforts should continue to be prioritized. 

f. Invest in the evidence base. This working paper reveals troubling racial disparities in the IRS’s 

audit selection process, but the fact that this research was conducted in the first place and 

released publicly is responsive to longstanding calls, from Dorothy Brown,31 Steven Dean,32

Jeremy Bearer-Friend,33 and other experts, for the IRS to release data that sheds light on the 

disparate impacts of tax policy and tax administration by race. It is also consistent with 

President Biden’s Executive Order 13985 from early 2021 on advancing racial equity through 

the federal government, which directs federal agencies to disaggregate administrative data by 

race.34

This study raises many new unanswered questions that will likely require further research and 

analysis. The Administration should prioritize investing in tax equity analysis across several 

offices, including Treasury’s Office of Tax Analysis (OTA), IRS Statistics of Income (SOI), and 

the IRS’s Research, Applied Analytics and Statistics Division (RAAS). Such investments are 

needed to provide lawmakers and the public with up-todate information on whether audit 

disparities are widening or shrinking over time, and to inform IRS and Treasury decisions on 

how to most effectively address these disparities. 

These steps would be consistent with President Biden’s recent Executive Order, released on February 

16, 2023, that directs federal agencies to “prevent and remedy discrimination, including by protecting 

the public from algorithmic discrimination.”35 They are also consistent with several rights enumerated 

in the Taxpayer Bill of Rights that IRS Commissioner nominee Werfel referenced during his 

30 Unenrolled tax preparers are paid tax preparers that are not attorneys, Certified Public Accountants (CPAs), or 

others with certifications verifying their competency in tax law. See: John Wancheck, “IRS Needs Authority to 

Regulate Tax Return Preparers,” Center on Budget and Policy Priorities, May 5, 2021, 

https://www.cbpp.org/blog/irs-needs-authority-to-regulate-tax-return-preparers.

31 Dorothy A. Brown, “The Whiteness of Wealth: How the Tax System Impoverishes Black Americans and How 

We Can Fix it,” New York, Crown, 2021. 

32 Steven Dean, “Filing While Black: The Casual Racism of the Tax Law,” Utah Law Review, Vol. 2022 No. 4, 

https://dc.law.utah.edu/cgi/viewcontent.cgi?article=1331&context=ulr.

33 Jeremy Bearer-Friend, “Colorblind Tax Enforcement,” NYU Law Review, Vol. 97 No. 1, April 2022, 

https://www.nyulawreview.org/issues/volume-97-number-1/colorblind-tax-enforcement/. 

34 The White House, “Executive Order On Advancing Racial Equity and Support for Underserved Communities 

Through the Federal Government,” Executive Order 13985, January 20, 2021, 

https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancingracialequityand-support-for-underserved-communities-through-the-federal-government/. 

35 The White House, supra note 2. 
confirmation hearing, including “the right to quality service” and “the right to a fair and just tax 

system.”36 

 

From: Murray Anderson

To: TACRE

Subject: Kansas is failing in the art of Advancing Racial Equity (American Rescue Plan Act) impact investment strategy is 

not in keeping with (MBDA) Calculating Parity of Minority Business Enterprises and the Expansion of the U.S.

Economy

Date: Tuesday, January 24, 2023 3:38:13 AM

** Caution: External email. Pay attention to suspicious links and attachments. Send suspicious email to 

suspect@treasury.gov **

Janis Bowdler Counselor to the Secretary (Racial Equity)

US Treasury 

Treasury Advisory Committee on Racial Equity (TACRE)

36 Internal Revenue Service, Taxpayer Bill of Rights, https://www.irs.gov/taxpayer-bill-of-rights. 
1500 Pennsylvania Avenue NW

Washington, DC 20220

US

Greetings Ms. Bowdler,

I trust you are well.

In accordance with our telephone message to your office at the Treasury Advisory Committee on Racial 

Equity (TACRE) regarding the ways and means the Modernization of Community Reinvestment Act 

through the LENS of the American Rescue Plan Act - Treasury Advisory

Committee on Racial Equity (TACRE) may in fact-in Realtime, implement the Minority

Business Development Agency {ECONOMIC PARITY}initiative as is EMBEDDED in the provisions of 

Executive Order 13985, Advancing Racial Equity and Support for Underserved Communities and thereto 

define {EQUITY IN CAPITAL TERMS} by removing the access to

capital barrier limiting the establishment and growth of MDI's and MBE's native to communities of color 

in the state of Kansas and FRS District (10) at large. 

Now herewith, I am formally requesting your assistance to develop and publish a Treasury

Advisory Committee on Racial Equity (TACRE) inspired CRA centric__ OMB_UST-ARPA allocation impact 

investment matrix for the state of Kansas along with the (return on investment) forecast model based 

on the ARPA allocation proposed investments collation to the US Treasury_ ARPA-ROI projection for 

communities of color and MBE'S in the State of Kansas.?

I trust we can agree that MBE'S Communities of color in the State of Kansas must seek to drive 

economic parity mantra as the way and means to advance financial and economic inclusion as 

prescribed by MBDA and in accordance with Executive Order 13985, Advancing Racial Equity and 

Support for Underserved Communities.

With that being said, please be advised of the following.

"The state of Kansas is failing in the art of Advancing Racial Equity. And further the state of Kansas 

(ARPA) miniscule equity investment strategy of less than 1% of $1 billion $669 million of ARPA 

funds in MBE's is not in keeping with (MBDA) Calculating Parity of Minority Business Enterprises 

vision and therefore because the lack of investment does not support the optimum growth and 

Expansion of the U.S. Economy." 

In conclusion, I concede to the following related MBDA Parity Papers and the Kansas State data facts.

“Parity would be reached when MBEs match non-MBE performance in terms of the number of 

firms per adult population, dollar receipts per firm, and number of paid employees per firm.”

1. 

MBDA Parity Paper - August 2021.pdf

(303.4 KB)
1. 

See the MBDA attachment:

 Calculating Parity of Minority Business Enterprises and the Expansion of the U.S. Economy 

Please respond at the earliest.

MDA, Executive Producer/Director

Ph. 913-210-0699 Cell. 913-314-0934

Email: murraydandersonsr@gmail.com

https://www.freedmensbanc.com/

Kansas - State Fact Sheets - FINAL2.pdf

(1.7 MB)

PEACE BE UNTO YOU!

Murray D. Anderson, Sr.
From: Tammy Hines

To: TACRE

Subject: Funding Mental Health equity research

Date: Sunday, December 4, 2022 2:53:34 AM

** Caution: External email. Pay attention to suspicious links and attachments. Send 

suspicious email to suspect@treasury.gov **

Oh Janis,

I need help with funding my continued research. I am 50 years old and own a behavioral

health private practice. My research will reveal why the mental health system is not going to

make it if we continue to believe organizations who take the funds but do not give it to

programs. Congress would slow down signing bills and research the bill's purpose and how it

makes black communities not get funded. I also wish they would slow down and return phone

calls. Racial equity will always be present if we don't give independent researchers funding.

Black Advocacy needs to be advertised.

I have 3 years of research that will answer questions and clear up some of these major issues

about lack of resources. I have had to slow down because no one will give me a grant,

scholarship, fellowship hell a cup of coffee. The mentally ill have money and you would be

shocked at how they are being stripped of their money. Too much funding going to Suicide

Prevention and policy. SAMHSA opened up 6 Certified Behavior Health Centers and not one

went to the black community, or the marginalized totaling 6 billion dollars (Guessing but I am

sure). They gave the same company 53 million for suicide prevention. HHS has not funded

black research in 20 or more years. National Alliance Of Mental Illness was given all mental

health funds totaling 3 trillion dollars by congress but none of it is given to mental health

professionals or the mentally ill. None is given to the people who make the money seeing

clients.

How did no other investigator find this out? NIH has been called out for being racially

insensitive for years. We have got to pay attention beyond their narratives and website

posting. Where are the whistleblowers? We need to create an audit system for everyone

who is awarded discretionary funds, our worst nightmare.

Please pass this to the person who needs to address it. I am passionate but tired of being

ignored.
--

Tammy Hines 

615-601-1028

Licensed Psychotherapist, Training

to be Investigative Journalist, Mental Health arrior, 

Fierce Advocate

"The day you are born and the day you find out why "Mark Twain" Born to Fight