Auditing Digital Platforms for Discrimination in Economic Opportunity Advertising
This work addresses systemic discrimination in digital advertising that affects marginalized groups by enabling audits, though it is incremental as it builds on existing audit methods.
The researchers tackled discrimination in economic opportunity advertising on digital platforms by developing a methodology and software to audit for bias, applying it to Facebook to analyze 141,063 ads and find skewed distributions by age and gender in regulated categories like housing and employment.
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.