  Digital platforms, including social networks, are major sources of economic
information. Evidence suggests that digital platforms display different
socioeconomic opportunities to demographic groups. Our work addresses this
issue by presenting a methodology and software to audit digital platforms for
bias and discrimination. To demonstrate, an audit of the Facebook platform and
advertising network was conducted. Between October 2019 and May 2020, we
collected 141,063 ads from the Facebook Ad Library API. Using machine learning
classifiers, each ad was automatically labeled by the primary marketing
category (housing, employment, credit, political, other). For each of the
categories, we analyzed the distribution of the ad content by age group and
gender. From the audit findings, we considered and present the limitations,
needs, infrastructure and policies that would enable researchers to conduct
more systematic audits in the future and advocate for why this work must be
done. We also discuss how biased distributions impact what socioeconomic
opportunities people have, especially when on digital platforms some
demographic groups are disproportionately excluded from the population(s) that
receive(s) content regulated by law.
