# Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy

arXiv: https://arxiv.org/abs/2011.07194 · HTML: https://mlsanity.com/paper/2011.07194/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level](https://mlsanity.com/paper/2203.04088/) (2022)
- [Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting](https://mlsanity.com/paper/2402.18697/) (2024)
- [Towards Algorithmic Fairness in Space-Time: Filling in Black Holes](https://mlsanity.com/paper/2211.04568/) (2022)
- [BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data](https://mlsanity.com/paper/2402.11318/) (2024)

## Abstract (arXiv)

Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet little attention has been paid to measurement validity and demographic bias, due in part to the lack of documentation about which users are represented as well as the challenge of obtaining ground truth data on unique visits and demographics. We illustrate how linking large-scale administrative data can enable auditing mobility data for bias in the absence of demographic information and ground truth labels. More precisely, we show that linking voter roll data -- containing individual-level voter turnout for specific voting locations along with race and age -- can facilitate the construction of rigorous bias and reliability tests. These tests illuminate a sampling bias that is particularly noteworthy in the pandemic context: older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.