An official website of the United States government
The 2022 settlement between Meta and the U.S. Department of Justice to resolve allegations of discriminatory advertising resulted is a first-of-its-kind change to Meta's ad delivery system aimed to address algorithmic discrimination in its housing ad delivery. In this work, we explore direct and indirect effects of both the settlement's choice of terms and the Variance Reduction System (VRS) implemented by Meta on the actual reduction in discrimination. \newline We first show that the settlement terms allow for an implementation that does not meaningfully improve access to opportunities for individuals. The settlement measures impact of ad delivery in terms of impressions, instead of unique individuals reached by an ad; it allows the platform to level down access, reducing disparities by decreasing the overall access to opportunities; and it allows the platform to selectively apply VRS to only small advertisers. \newline We then conduct experiments to evaluate VRS with real-world ads, and show that while VRS does reduce variance, it also raises advertiser costs (measured per-individuals-reached), therefore decreasing user exposure to opportunity ads for a given ad budget. VRS thus \emph{passes the cost of decreasing variance to advertisers}. \newline Finally, we explore an alternative approach to achieve the settlement goals, that is significantly more intuitive and transparent than VRS. We show our approach outperforms VRS by both increasing ad exposure for users from \emph{all} groups and reducing cost to advertisers, thus demonstrating that the increase in cost to advertisers when implementing the settlement is not inevitable. \newline Our methodologies use a black-box approach that relies on capabilities available to any regular advertiser, rather than on privileged access to data, allowing others to reproduce or extend our work.
more »
« less
This content will become publicly available on June 23, 2026
Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes
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 \emph{infer} them from other available information. In this work, we study the effects of inference error on auditing for bias in one prominent application: \emph{black-box} audit of ad delivery using \emph{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.
more »
« less
- PAR ID:
- 10632613
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714825
- Page Range / eLocation ID:
- 2640 to 2656
- Format(s):
- Medium: X
- Location:
- Athens Greece
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Discussion of the “right to an explanation” has been increasingly relevant because of its potential utility for auditing automated decision systems, as well as for making objections to such decisions. However, most existing work on explanations focuses on collaborative environments, where designers are motivated to implement good-faith explanations that reveal potential weaknesses of a decision system. This motivation may not hold in an auditing environment. Thus, we ask: how much could explanations be used maliciously to defend a decision system? In this paper, we demonstrate how a black-box explanation system developed to defend a black-box decision system could manipulate decision recipients or auditors into accepting an intentionally discriminatory decision model. In a case-by-case scenario where decision recipients are unable to share their cases and explanations, we find that most individual decision recipients could receive a verifiable justification, even if the decision system is intentionally discriminatory. In a system-wide scenario where every decision is shared, we find that while justifications frequently contradict each other, there is no intuitive threshold to determine if these contradictions are because of malicious justifications or because of simplicity requirements of these justifications conflicting with model behavior. We end with discussion of how system-wide metrics may be more useful than explanation systems for evaluating overall decision fairness, while explanations could be useful outside of fairness auditing.more » « less
-
We consider the problem of allocating divisible items among multiple agents, and consider the setting where any agent is allowed to introduce {\emph diversity constraints} on the items they are allocated. We motivate this via settings where the items themselves correspond to user ad slots or task workers with attributes such as race and gender on which the principal seeks to achieve demographic parity. We consider the following question: When an agent expresses diversity constraints into an allocation rule, is the allocation of other agents hurt significantly? If this happens, the cost of introducing such constraints is disproportionately borne by agents who do not benefit from diversity. We codify this via two desiderata capturing {\em robustness}. These are {\emph no negative externality} -- other agents are not hurt -- and {\emph monotonicity} -- the agent enforcing the constraint does not see a large increase in value. We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is {\emph uniquely} positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules. We finally perform an empirical simulation on real-world data that models ad allocations to show that this gap between Nash Welfare and other rules persists in the wild.more » « less
-
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners’ current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and mitigate risks) of user-engaged auditing in industry practice.more » « less
-
Levy, Morris (Ed.)Abstract Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions—e.g. based on name and geography—and then to often discretize the predictions by selecting the most likely class (argmax), potentially with a minimum threshold (thresholding). We study how this practice produces discretization bias. For example, we show that argmax labeling, as used by a prominent commercial voter file vendor to impute race/ethnicity, results in a substantial under-count of Black voters, e.g. by 28.2% points in North Carolina. This bias can have substantial implications in downstream tasks that use such labels. We then introduce a joint optimization approach—and a tractable data-driven threshold heuristic—that can eliminate this bias, with negligible individual-level accuracy loss. Finally, we theoretically analyze discretization bias, show that calibrated continuous models are insufficient to eliminate it, and that an approach such as ours is necessary. Broadly, we warn researchers and practitioners against discretizing continuous demographic predictions without considering downstream consequences.more » « less
