# The Impact of Differential Feature Under-reporting on Algorithmic Fairness

arXiv: https://arxiv.org/abs/2401.08788 · HTML: https://mlsanity.com/paper/2401.08788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Assessing Fairness in the Presence of Missing Data](https://mlsanity.com/paper/2112.04899/) (2021)
- [Algorithms and Decision-Making in the Public Sector](https://mlsanity.com/paper/2106.03673/) (2021)
- [Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models](https://mlsanity.com/paper/2206.09875/) (2022)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [The Challenge of Imputation in Explainable Artificial Intelligence Models](https://mlsanity.com/paper/1907.12669/) (2019)
- [Learning Models from Data with Measurement Error: Tackling Underreporting](https://mlsanity.com/paper/1901.09060/) (2019)

## Built on by (most cited first, 4 of 4)

- [Improving LLM Group Fairness on Tabular Data via In-Context Learning](https://mlsanity.com/paper/2412.04642/) (2024)
- [To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling](https://mlsanity.com/paper/2407.19114/) (2024)
- [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)

## Abstract (arXiv)

Predictive risk models in the public sector are commonly developed using administrative data that is more complete for subpopulations that more greatly rely on public services. In the United States, for instance, information on health care utilization is routinely available to government agencies for individuals supported by Medicaid and Medicare, but not for the privately insured. Critiques of public sector algorithms have identified such differential feature under-reporting as a driver of disparities in algorithmic decision-making. Yet this form of data bias remains understudied from a technical viewpoint. While prior work has examined the fairness impacts of additive feature noise and features that are clearly marked as missing, the setting of data missingness absent indicators (i.e. differential feature under-reporting) has been lacking in research attention. In this work, we present an analytically tractable model of differential feature under-reporting which we then use to characterize the impact of this kind of data bias on algorithmic fairness. We demonstrate how standard missing data methods typically fail to mitigate bias in this setting, and propose a new set of methods specifically tailored to differential feature under-reporting. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities. The proposed solution methods show success in mitigating increases in unfairness.