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Anonymized smartphone-based mobility data has been widely adopted in devisingand evaluating COVID-19 response strategies such as the targeting of publichealth resources. Yet little attention has been paid to measurement validityand demographic bias, due in part to the lack of documentation about whichusers are represented as well as the challenge of obtaining ground truth dataon unique visits and demographics. We illustrate how linking large-scaleadministrative data can enable auditing mobility data for bias in the absenceof demographic information and ground truth labels. More precisely, we showthat linking voter roll data -- containing individual-level voter turnout forspecific voting locations along with race and age -- can facilitate theconstruction of rigorous bias and reliability tests. These tests illuminate asampling bias that is particularly noteworthy in the pandemic context: olderand non-white voters are less likely to be captured by mobility data. We showthat allocating public health resources based on such mobility data coulddisproportionately harm high-risk elderly and minority groups.

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