  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.
