  This study examines issues of algorithmic fairness in the context of systems
that inform tax audit selection by the United States Internal Revenue Service
(IRS). While the field of algorithmic fairness has developed primarily around
notions of treating like individuals alike, we instead explore the concept of
vertical equity -- appropriately accounting for relevant differences across
individuals -- which is a central component of fairness in many public policy
settings. Applied to the design of the U.S. individual income tax system,
vertical equity relates to the fair allocation of tax and enforcement burdens
across taxpayers of different income levels. Through a unique collaboration
with the Treasury Department and IRS, we use access to anonymized individual
taxpayer microdata, risk-selected audits, and random audits from 2010-14 to
study vertical equity in tax administration. In particular, we assess how the
use of modern machine learning methods for selecting audits may affect vertical
equity. First, we show how the use of more flexible machine learning
(classification) methods -- as opposed to simpler models -- shifts audit
burdens from high to middle-income taxpayers. Second, we show that while
existing algorithmic fairness techniques can mitigate some disparities across
income, they can incur a steep cost to performance. Third, we show that the
choice of whether to treat risk of underreporting as a classification or
regression problem is highly consequential. Moving from classification to
regression models to predict underreporting shifts audit burden substantially
toward high income individuals, while increasing revenue. Last, we explore the
role of differential audit cost in shaping the audit distribution. We show that
a narrow focus on return-on-investment can undermine vertical equity. Our
results have implications for the design of algorithmic tools across the public
sector.

Machine Learning;Computers and Society