To audit an ad campaign for demographic bias, compare who was eligible to see it with who actually received impressions, and separate the advertiser’s targeting choices from the platform’s delivery optimization. A delivery gap is a reason to investigate—not, by itself, proof of its cause or of unlawful discrimination. For a finance advertiser, first identify whether the campaign concerns a regulated category such as credit and which jurisdiction’s rules apply.
What should an ad-bias audit measure?
Measure both the campaign’s settings and its delivery. A review of targeting settings alone can miss disparities caused by platform optimization, inferred interests, lookalike audiences, creative, campaign objectives, budget choices or competition in the ad auction.
Define the eligible audience before looking at results. The right comparison is not automatically the general population: it is the people who were available to receive the ad under the campaign’s legitimate eligibility or qualification criteria and actual settings. For example, an audit should account for genuine qualification requirements rather than treating every difference from the overall population as exclusion.
Where demographic reporting is available and can be used lawfully, compare each group’s share of impressions with its share of the eligible audience. A descriptive measure is:
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Impression share for a group = that group’s impressions ÷ all campaign impressions.
Eligible-audience share for a group = eligible people in that group ÷ all eligible people.
The difference between those shares can identify a delivery gap for investigation. It is not a universal fairness threshold or a legal test. Record how demographic attributes were obtained, how missing or uncertain data were treated, and whether group-level reporting is too sparse to support a reliable comparison.
Where can demographic exclusion enter?
- Advertiser choices: audience definitions, exclusions, geographic settings, placements, budget and bidding decisions can shape who is eligible or likely to see an ad.
- Platform delivery: optimization for a campaign objective can distribute impressions unevenly even when the advertiser has not expressly selected a protected group.
- Proxies and inferred attributes: behavior, location, inferred interests and lookalike audiences may produce patterns associated with demographic characteristics without explicit demographic targeting.
- Creative and destination: the ad itself or its landing page may affect which people respond, which can influence later delivery optimization.
- Market conditions: audience availability and competing advertisers can affect impressions, so a difference in delivery does not identify its cause on its own.
In January 2023 EEOC testimony, witness ReNika Moore described how employment ads could be delivered based on real or inferred personal characteristics and predicted interests. That testimony is useful context for proxy effects; it is not a finding about a particular advertiser or a current determination about any platform.
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How do you run a practical audit?
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Define scope and preserve the setup
Record the platform, campaign dates, geography, ad category, objective, budget, audience definition, exclusions, placements, creative and destination page. Note which demographic groups and forms of exclusion matter to the campaign and jurisdiction. Save the campaign settings and any changes made during the observation period.
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Write down the eligible-audience baseline
Before examining delivery, state who could legitimately receive the ad under the actual eligibility criteria and settings. Explain any qualifications, geographic limits or exclusions and why they apply. Use the same definition throughout the comparison; changing the denominator after seeing the results can obscure rather than clarify a disparity.
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Separate targeting from delivery
Document what the advertiser selected or excluded, then record the campaign objective and optimization settings that shaped platform delivery. Also note creative, budget, bidding choices and relevant competition where known. This separation helps identify what the audit can observe directly and what remains a possible explanation rather than an established cause.
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Inspect actual outcomes
Export available impression and audience reports for the observation window. Compare delivery across the groups relevant to the audit against the eligible-audience baseline, not just against the campaign’s intended targeting. Record whether platform reports provide direct demographic breakdowns or only estimates, and identify groups for which data are unavailable or too limited.
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Use matched comparisons where feasible
Compare campaigns or ads that ran at the same time and have similar eligibility or qualification requirements, while varying the factor the audit is meant to investigate. Keep objective, creative, budget, audience availability and other relevant settings as similar as possible, and document differences that could not be controlled. A matched comparison can reduce some alternative explanations; it does not remove every confounder or create a universal legal test.
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Keep the analysis reproducible
Preserve report exports, dates, settings, group definitions, exclusions, data transformations, comparison design and assumptions. Record uncertainty, missing data and limits on access to platform information. Where privacy rules constrain demographic analysis, use only data and methods permitted for the relevant jurisdiction and campaign.
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Report the conclusion at the strength the evidence supports
State separately whether a disparity was observed, what mechanisms were tested, and what remains unknown. Do not describe a correlation as proof that targeting or an algorithm caused the gap. Note whether incomplete reporting, changing auction conditions, small samples or unobserved eligibility differences could alter the interpretation.
Which audit approach should you use?
Different approaches answer different questions. The following comparison is a practical framework, not a single standard prescribed for every campaign.
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| Approach | What it can show | Main limitation |
|---|---|---|
| Settings review | Advertiser-defined audiences, exclusions and other visible campaign choices. | Does not establish who actually received impressions or explain optimization effects. |
| Delivery review | Observed impressions by relevant groups, if reporting supports that breakdown. | May not reveal platform internals or provide sufficiently detailed demographic data. |
| Matched comparison | Whether delivery differs across similar campaigns or ads while selected factors are held as constant as practicable. | Requires defensible matching and careful documentation; unobserved differences may remain. |
When choosing, consider whether the method measures advertiser targeting, platform delivery or both; how it defines the eligible audience; whether it can observe impressions by relevant demographics; which factors it can control; what privacy safeguards and data access it requires; and whether another auditor could reproduce the analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does published research establish—and what does it not?
Imana, Korolova and Heidemann’s 2021 study, “Auditing for Discrimination in Algorithms Delivering Job Ads,” proposed a matched-ad method and applied it to Facebook and LinkedIn. The authors reported statistically significant gender skew in their Facebook job-ad experiment and did not find such skew in their LinkedIn experiment. Those are results from the authors’ study design, platforms and period—not measurements of current platform behavior.
The study’s paired job ads ran at the same time and involved jobs with similar qualification requirements but different existing workforce gender distributions. The researchers used that comparison to reduce the chance that qualification differences explained delivery patterns, while controlling for other factors outside platform delivery. Their approach illustrates how to frame a test; its assumptions need to be assessed for each campaign.
The authors also noted that outside auditors may lack access to platform algorithms and user data, and may have to rely on platform-provided statistics that do not contain enough demographic detail for direct measurement. A black-box audit can surface patterns and test hypotheses, but its conclusions must reflect those data limits.
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How do legal and regulatory rules affect the audit?
Do not treat platform transparency duties, anti-discrimination law and voluntary fairness practices as interchangeable. Applicable rules depend on jurisdiction, campaign category, protected classes and facts. In a finance context, determine whether the campaign concerns credit or another regulated product and seek qualified legal advice before treating a statistical result as a legal conclusion.
In the United States employment context, the EEOC witness testimony discussed targeting through personal characteristics, online behavior, inferred interests, location and lookalike audiences. It describes why restrictions on direct targeting alone may not address every potential proxy or delivery effect; it does not decide whether a particular campaign violates law.
The European Commission’s overview of the Digital Services Act says ads must be labeled and very large online platforms must maintain repositories with details about paid campaigns. It also describes a prohibition on targeted advertising on online platforms where profiling uses special categories of personal data, including ethnicity, political views or sexual orientation. The exact application depends on the service and facts.
Commission Delegated Regulation (EU) 2024/436 recognizes advertising systems among algorithmic systems that may be audited under the DSA. It describes a methodology combining assessment of internal controls, substantive analytical procedures and, where appropriate, system tests, with evidence that is sufficient, appropriate and reliable. It does not prescribe one demographic-parity metric for every advertiser’s campaign.
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