Tag: Discrimination

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new technical report “Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes”

We have released a new technical report: “Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes”, available at https://arxiv.org/abs/2410.23394.

From the abstract:

[Imana23c, figure 3]: Detecting racial skew with BISG-based inference is less sensitive (shown by the lower test statistic Z) than either knowing true-race, or using our improved version that reflects potential inference error. More samples and larger underlying skew make the range of confusion smaller, but do not eliminate it.
[Imana23c, figure 3]: Detecting racial skew with BISG-based inference is less sensitive (shown by the lower test statistic Z) than either knowing true-race, or using our improved version that reflects potential inference error. More samples and larger underlying skew make the range of confusion smaller, but do not eliminate it.

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 technical report is joint work of Basilial Imana and Aleksandra Korolova (both of Princeton) and John Heidemann (USC/ISI). This work was supported by the NSF via CNS-1956435, CNS-2344925, and CNS-2319409 (the InternetMap project).

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Papers Publications

New conference paper: Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest

Our new paper “Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest” will appear at The 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2023).

From the abstract:

Overview of our proposed platform-supported framework for auditing relevance estimators while protecting the privacy of audit participants and the business interests of platforms.

Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users’ private data and platforms’ proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms’ business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4x for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

A 2-minute video overview of the work can be found here.

This paper is a joint work of Basileal Imana from USC, Aleksandra Korolova from Princeton University, and John Heidemann from USC/ISI.

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Technical Report

new technical report: Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest

We have released a new technical report: “Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest”, available at https://arxiv.org/abs/2207.08773.

From the abstract:

Legislations have been proposed in both the U.S. and the E.U. that mandate auditing of social media algorithms by external researchers. But auditing at scale risks disclosure of users’ private data and platforms’ proprietary algorithms, and thus far there has been no concrete technical proposal that can provide such auditing. Our goal is to propose a new method for platform-supported auditing that can meet the goals of the proposed legislations. The first contribution of our work is to enumerate these challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms’ business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34× as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

High-level overview of our proposed platform-supported framework for auditing relevance estimators while protecting the privacy of audit participants and the business interests of platforms.

This technical report is a joint work of Basileal Imana from USC, Aleksandra Korolova from Princeton University, and John Heidemann from USC/ISI.

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Data Papers Publications

New paper “Auditing for Discrimination in Algorithms Delivering Job Ads” at TheWebConf 2021

We published a new paper “Auditing for Discrimination in Algorithms Delivering Job Ads” by Basileal Imana (University of Southern California), Aleksandra Korolova (University of Southern California) and John Heidemann (University of Southern California/ISI) at TheWebConf 2021 (WWW ’21).

From the abstract:

Skew in the delivery of real-world ads on Facebook (FB) but not LinkedIn (LI).
Comparison of ad delivery using “Reach” (R) and “Conversion” (C) campaign objectives on Facebook. There is skew for both cases but less skew for “Reach”.

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform’s optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.

This paper was awarded runner-up for best student paper at The Web Conference 2021.

The data from this paper is upon request, please see our dataset page.

This work was reported in the popular press: The Intercept, MIT Technology Review, Wall Street Journal, The Register, VentureBeat, Reuters, The Verge, Engadget, Associated Press.