Our new paper “Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes” (PDF) will appear at The eighth annual ACM FAccT conference (FAccT 2025) being held from June 23-26, 2025 in Athens, Greece.
From the abstract:
Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics data is unavailable, auditors commonly infer them from other available information. In this work, we study the effects of inference error on auditing for bias in one prominent application: black-box audit of ad delivery using paired ads. We show that inference error, if not accounted for, causes auditing to falsely miss skew that exists. We then propose a way to mitigate the inference error when evaluating skew in ad delivery algorithms. Our method works by adjusting for expected error due to demographic inference, and it makes skew detection more sensitive when attributes must be inferred. Because inference is increasingly used for auditing, our results provide an important addition to the auditing toolbox to promote correct audits of ad delivery algorithms for bias. While the impact of attribute inference on accuracy has been studied in other domains, our work is the first to consider it for black-box evaluation of ad delivery bias, when only aggregate data is available to the auditor.
This paper is a joint work of Basileal Imana and Aleksandra Korolova from Princeton University, and John Heidemann from USC/ISI. This work was supported in part by NSF grants CNS-1956435, CNS-2344925, and CNS-2319409.