Tradeoffs are Domain Dependent: Improving Accuracy and Fairness in Property Tax Assessments

• URL: http://arxiv.org/abs/2605.15020v1
• Date: Thu, 14 May 2026 16:20:32 GMT
• Title: Tradeoffs are Domain Dependent: Improving Accuracy and Fairness in Property Tax Assessments
• Abstract summary: Algorithmic fairness research often assumes a tradeoff between fairness and accuracy. Yet this tradeoff may not be universal.<n>We test this assumption in the context of U.S. property tax assessment.
• Score: 7.885461849343874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic fairness research often assumes a tradeoff between fairness and accuracy. Yet this tradeoff may not be universal. We test this assumption in the context of U.S. property tax assessment - a setting in which the output of predictive algorithms directly determines the distribution of tax obligations among homeowners. Currently, systematic assessment errors cause owners of lower-valued properties to face disproportionately high tax burdens, creating regressivity in the property tax system. Using data on 26 million property sales spanning 95% of U.S. counties, we conduct three complementary analyses. First, we find that assessment accuracy and fairness - measured using domain-relevant metrics - are strongly correlated across counties under status quo practices. Second, in simulated assessment models, we show that adding property features improves accuracy in most cases, and that when accuracy improves, fairness almost always improves as well. Third, we show that incorporating publicly available Census data into assessment models - a feasible reform in most counties - would significantly improve both accuracy and fairness relative to status quo assessments. Together, these results challenge the presumed universality of the fairness-accuracy tradeoff and demonstrate that well-designed modeling improvements can advance both fairness and accuracy in large-scale public sector systems.

Related papers

• Hidden Errors in Big Data: The Case of Property Records [7.339927315483204]
  We audit two prominent brokered property datasets in Cook County, IL.<n>For 1-2% of matched sales, broker-provided sale prices differ from ground truth sale prices by more than 5%.<n>We show that misreporting is highly consistent between brokers.
  arXiv  Detail & Related papers  (2026-07-30T20:36:06Z)
• Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers [56.54960821093771]
  We conduct the first systematic comparison of fairness between reasoning and non-reasoning rerankers.<n>Our findings demonstrate reasoning neither improve nor harm fairness compared to non-reasoning approaches.
  arXiv  Detail & Related papers  (2026-03-11T02:01:24Z)
• Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics [96.68144350976637]
  Fairness is an increasingly important factor in re-ranking tasks.<n>The accuracy-fairness trade-off parallels the coupling of the commodity tax transfer process.<n>We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework.<n>We also propose ElasticRank, a fair re-ranking algorithm that employs elasticity calculations to adjust inter-item distances.
  arXiv  Detail & Related papers  (2025-04-21T09:41:08Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods.<n>We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results.<n>We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds [8.471466670802817]
  We study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy as a function of the fairness budget. Our results show that achieving high accuracy subject to a low-bias could be fundamentally limited based on the statistical disparity across the groups.
  arXiv  Detail & Related papers  (2024-05-12T23:15:21Z)
• A Taxation Perspective for Fair Re-ranking [61.946428892727795]
  We introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items. Our model Tax-rank offers a superior tax policy for fair re-ranking, theoretically demonstrating both continuity and controllability over accuracy loss.
  arXiv  Detail & Related papers  (2024-04-27T08:21:29Z)
• Fairness Under Demographic Scarce Regime [7.523105080786704]
  We propose a framework to build attribute classifiers that achieve better fairness-accuracy tradeoffs. We show that enforcing fairness constraints on samples with uncertain sensitive attributes can negatively impact the fairness-accuracy tradeoff. Our framework can outperform models trained with fairness constraints on the true sensitive attributes in most benchmarks.
  arXiv  Detail & Related papers  (2023-07-24T19:07:34Z)
• On Comparing Fair Classifiers under Data Bias [42.43344286660331]
  We study the effect of varying data biases on the accuracy and fairness of fair classifiers. Our experiments show how to integrate a measure of data bias risk in the existing fairness dashboards for real-world deployments.
  arXiv  Detail & Related papers  (2023-02-12T13:04:46Z)
• Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models [73.24381010980606]
  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.
  arXiv  Detail & Related papers  (2022-06-20T16:27:06Z)

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.