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This study examines issues of algorithmic fairness in the context of systemsthat inform tax audit selection by the United States Internal Revenue Service(IRS). While the field of algorithmic fairness has developed primarily aroundnotions of treating like individuals alike, we instead explore the concept ofvertical equity -- appropriately accounting for relevant differences acrossindividuals -- which is a central component of fairness in many public policysettings. Applied to the design of the U.S. individual income tax system,vertical equity relates to the fair allocation of tax and enforcement burdensacross taxpayers of different income levels. Through a unique collaborationwith the Treasury Department and IRS, we use access to anonymized individualtaxpayer microdata, risk-selected audits, and random audits from 2010-14 tostudy vertical equity in tax administration. In particular, we assess how theuse of modern machine learning methods for selecting audits may affect verticalequity. First, we show how the use of more flexible machine learning(classification) methods -- as opposed to simpler models -- shifts auditburdens from high to middle-income taxpayers. Second, we show that whileexisting algorithmic fairness techniques can mitigate some disparities acrossincome, they can incur a steep cost to performance. Third, we show that thechoice of whether to treat risk of underreporting as a classification orregression problem is highly consequential. Moving from classification toregression models to predict underreporting shifts audit burden substantiallytoward high income individuals, while increasing revenue. Last, we explore therole of differential audit cost in shaping the audit distribution. We show thata narrow focus on return-on-investment can undermine vertical equity. Ourresults have implications for the design of algorithmic tools across the publicsector.

Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models