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

• URL: http://arxiv.org/abs/2206.09875v1
• Date: Mon, 20 Jun 2022 16:27:06 GMT
• Title: Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
• Authors: Emily Black, Hadi Elzayn, Alexandra Chouldechova, Jacob Goldin, Daniel E. Ho
• Abstract summary: This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS. We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity. Our results have implications for the design of algorithmic tools across the public sector.
• Score: 73.24381010980606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes [50.37313459134418]
  We study the effects of inference error on auditing for bias in one prominent application: black-box audit of ad delivery using paired ads. We propose a way to mitigate the inference error when evaluating skew in ad delivery algorithms.
  arXiv  Detail & Related papers  (2024-10-30T18:57:03Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• Error Parity Fairness: Testing for Group Fairness in Regression Tasks [5.076419064097733]
  This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
  arXiv  Detail & Related papers  (2022-08-16T17:47:20Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions [4.159343412286401]
  We argue that algorithmic unfairness stems from interactions between models and biases in the data. We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
  arXiv  Detail & Related papers  (2022-07-13T15:18:30Z)
• Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection [2.2182596728059116]
  We introduce a new setting, optimize-and-estimate structured bandits. This setting is inherent to many public and private sector applications. We demonstrate its importance on real data from the United States Internal Revenue Service.
  arXiv  Detail & Related papers  (2022-04-25T18:28:55Z)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness Notions [1.8275108630751837]
  This paper proposes a new fairness notion based on the principle of non-comparative justice. We show that any MLS can be deemed fair from the perspective of comparative fairness. We also show that the converse holds true in the context of individual fairness.
  arXiv  Detail & Related papers  (2021-06-29T20:05:22Z)
• All of the Fairness for Edge Prediction with Optimal Transport [11.51786288978429]
  We study the problem of fairness for the task of edge prediction in graphs. We propose an embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary graph with a trade-off between the group and individual fairness.
  arXiv  Detail & Related papers  (2020-10-30T15:33:13Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.