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Audit gender representation by testing a predeclared prompt matrix, generating repeatable samples, labeling both who appears and how people are depicted, and comparing results with a documented benchmark. A single striking image cannot establish fairness. Record the model version, prompts, seeds and settings, date, reviewers, exclusions, and uncertainty; then rerun the same protocol after model, policy, or prompt changes.
The most useful audit separates representational bias (which people appear, in which roles and numbers) from presentational bias (pose, camera angle, clothing, expression, agency, or sexualization). It also reports intersections such as race, age, disability, culture, occupation, and role, because an overall percentage can conceal severe disparities for a subgroup.
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A credible audit measures two related but different constructs and states the limits of visual inference. Do not collapse them into one fairness score.
| Construct | Questions to code | Typical outputs |
|---|---|---|
| Representational bias | Who is visible? How many people appear? What apparent role, occupation, age group, or setting is shown? Who has agency or performs the requested task? | Presence rates, role distributions, disparity from a benchmark, unknown and not-visible rates |
| Presentational bias | How are people posed, framed, styled, emotionalized, clothed, or sexualized? Who is shown as active, authoritative, vulnerable, or in the background? | Camera-angle, body-position, expression, styling, agency, and sexualization rates |
Labels such as “woman” or “man” inferred from appearance are proxies, not facts about identity. Use self-identification when it is available; otherwise record “unknown” rather than assigning a gender from hair, body shape, clothing, voice, or facial features. Include a nonbinary/other option only where the person’s self-identification or the task context supports it.
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The UK Information Commissioner’s Office says datasets should be “accurate, complete and representative of the purpose of the processing.” Its statistical-accuracy guidance also calls for labeling blind spots and testing “edge cases” (rare or unusual situations). These principles apply to generated media: define what you can observe, document what you cannot, and do not turn an uncertain visual judgment into a definitive identity claim.
How do I define the audit scope before generating anything?
Write a one-page scope statement before viewing outputs. It prevents a convenient prompt or memorable image from becoming the de facto test.
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- Geography and population: specify the country or region, reference population, language, and intended audience. A US labor benchmark cannot automatically represent another country.
- Date and change boundary: record the generation date and define which model, policy, prompt-template, or interface changes require a rerun.
- Media unit: state whether you are auditing generated pixels, edited images, captions, video scenes, audio-visual output, or the full publication workflow.
- Construct definitions: define “gender representation,” “role,” “agency,” “sexualization,” and every category in the codebook before sampling.
- Decision threshold: state what would trigger remediation, escalation, or a larger sample. Avoid changing the threshold after seeing results.
Keep identity categories separate from presentation categories. For example, “apparent woman” is a presence label; “smiling,” “low camera angle,” or “revealing clothing” are presentation labels. A person can receive any combination of those labels, including unknown identity and observable presentation.
How do I build a prompt matrix that can reveal stereotypes?
Use a balanced matrix rather than a handful of natural-language examples. The matrix is the audit’s sampling frame, so publish its wording and selection rules.
Choose role and control cells
Include occupational and everyday-role prompts, with male-coded, female-coded, mixed, and deliberately non-stereotyped roles. Pair roles that have comparable wording and visual context, such as “a software engineer presenting a design” and “a nurse explaining a treatment,” then add neutral controls such as “a professional at work.” Do not rely only on occupations; family, caregiving, leadership, education, sport, science, and leisure can expose different patterns.
Add intersectional cells
Where relevant to deployment, cross role prompts with age, race or ethnicity, disability, culture, language, and socioeconomic setting. Record which combinations are intentionally sampled and which are out of scope. An aggregate “women” rate can look balanced while older women, disabled men, or nonbinary people are absent from particular roles.
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Predeclare wording and generation rules
Freeze spelling, grammatical gender, language, punctuation, negative prompts, aspect ratio, guidance or quality settings, safety settings, and post-processing. Specify the number of generations per cell, how seeds are selected or randomized, and what counts as an invalid output. If the interface does not expose a seed, store the run identifier and all available settings.
| Matrix element | What to document |
|---|---|
| Prompt text | Exact string, language, capitalization, and any system or negative prompt |
| Cell definition | Role, control type, intersections, and intended comparison |
| Sampling | Generations per cell, randomization or seed rule, and holdout allocation |
| Rendering | Model mode, aspect ratio, quality, safety and style settings |
| Exclusions | Technical failures, refusals, duplicates, and the rule for retaining or analyzing them |
How should I generate and preserve the sample?
Run the identical matrix through the model version under test and preserve original outputs. A reproducible sample is more valuable than a large but undocumented folder.
- Prepare a run manifest. Give every prompt cell and output a stable identifier. Include prompt text, model identifier, interface or API version, settings, seed or randomization details, timestamp, operator, and intended benchmark.
- Generate without selective stopping. Complete the predeclared number of attempts for every cell. Record refusals, safety-filter messages, timeouts, malformed images, and duplicates instead of silently replacing them.
- Store originals and derivatives separately. Preserve the untouched file, metadata, and output identifier. Keep crops, color corrections, frame extracts, and captions in a linked derivatives folder.
- Reserve a holdout set. Keep prompts or generations that are not used to tune the codebook or remediation decisions. Use them for validation after the protocol is frozen.
- Sample video consistently. Define a rule such as fixed time intervals, scene changes, or all scenes containing people. Retain temporal context so a single frame does not misrepresent who acts, speaks, or leads over time.
Version the model, policy configuration, prompt template, date, and post-processing. Keep an audit trail that lets another reviewer recreate the sample or identify exactly what changed between runs.
What should the human codebook contain?
Write label definitions and examples before reviewers see the results. The codebook should distinguish observable properties from interpretations and make uncertainty a valid outcome.
Presence and role fields
- Person present: yes, no, or unclear.
- Number of people and visibility: count, partially visible, face not visible, or obscured.
- Gender identity: self-identified category where available; otherwise unknown. Include nonbinary/other and not applicable when supported by the context.
- Apparent role or occupation: the requested role, another role, mixed roles, or unknown.
- Action and agency: initiating, directing, performing skilled work, assisting, observing, background, or indeterminate.
Presentation fields
- Camera angle and framing, including close-up, full body, elevated, or low-angle views.
- Body position and spatial prominence, such as foreground, center, seated, standing, or physically subordinate placement.
- Facial expression and emotional framing, including smiling, neutral, distressed, confident, or unreadable.
- Clothing, styling, and props, coded descriptively rather than as moral judgments.
- Sexualization indicators defined in advance, such as emphasis on breasts, buttocks, cleavage, nudity, or suggestive posing.
Provide “unknown,” “not visible,” “not applicable,” and “unable to code” options. Never force a label because a statistical table looks cleaner. For video, code both frame-level presentation and scene-level agency so a transient pose is not treated as the character’s whole depiction.
Use independent reviewers and adjudication
Have at least two independent reviewers label a subset or the full sample. Include people with relevant domain knowledge and lived experience of the groups being evaluated. After independent coding, adjudicate disagreements using the written definitions, record the final decision, and report inter-rater agreement rather than hiding it. Reviewers should be allowed to flag a category as unobservable or culturally ambiguous.
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What benchmark should I compare with?
Choose a benchmark that matches the question and document its year, geography, population, collection method, inclusion rules, and uncertainty. A benchmark is a reference distribution, not a universal truth about gender.
| Audit question | Suitable benchmark | Important qualification |
|---|---|---|
| Are occupational groups present in expected proportions? | Current, geography-specific census or labor statistics | Align occupation definitions, working-age population, geography, and benchmark year. |
| Are poses, styling, or emotional depictions skewed? | A carefully sampled human-media corpus with documented genre and audience | Genre, casting, editorial choices, and camera conventions can create differences unrelated to the model. |
| Does output meet a product’s intended audience target? | A project-specific target distribution | Explain who set the target and why it is appropriate; do not present it as a population fact. |
A 2024 Journal of Computer-Mediated Communication study analyzed 15,300 DALL·E 2 images across 153 occupations, comparing them with 2021 US Census labor statistics and Google Images. It reported underrepresentation of women in male-dominated occupations, overrepresentation in female-dominated occupations, and evidence that the model amplified bias relative to its benchmarks. Those findings are specific to DALL·E 2, the study’s prompts, and the 2021 US comparison; they do not establish identical rates for every current generator or country.
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An Australian eSafety Commissioner guide reported a separate study of 99 images from nine text-to-image generators: professionals were represented as men in 76% of images and women in 8%, while doctors were represented as women in 7%. Because the sample was small and the figures are a secondary report, use them as context and not as a universal estimate of model behavior.
How do I calculate and visualize disparity?
Report counts before percentages, then show rates by prompt cell, benchmark difference, uncertainty, and missingness. Separate representation charts from presentation charts.
- Representation rate: number of outputs containing the coded category divided by valid outputs in that cell.
- Benchmark disparity: output rate minus the corresponding benchmark rate, with the benchmark definition beside the number.
- Intersectional rate: the same calculation within a subgroup intersection, such as age × gender × occupation.
- Unknown and refusal rate: report outputs that cannot be responsibly labeled, plus refusals and technical failures, rather than dropping them without explanation.
- Uncertainty: provide confidence or credible intervals appropriate to the sampling design, and flag sparse cells. Avoid ranking tiny differences as meaningful.
Use small-multiple charts or faceted tables so role and intersection remain visible. One overall “female percentage” can conceal that women appear frequently in caregiving prompts but rarely in leadership prompts. For presentation, show separate distributions for camera angle, agency, expression, styling, and sexualization; do not combine unlike variables into a single score without a defensible weighting method.
When comparing model versions, keep the prompt matrix, settings, sample size, benchmark, and labeling process constant. If you change prompts or reviewers, treat the result as a new experiment and explain the break in comparability.
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How can I stress-test an apparent finding?
Try to falsify a disparity before treating it as a model property. Rerun the protocol with fresh generations and test prompts that target known blind spots.
- Edge cases: ambiguous occupations, rare roles, unusual clothing, older adults, visible and non-visible disabilities, and scenes with multiple leaders.
- Counter-stereotypical prompts: explicitly reverse expected gender-role associations while keeping wording and visual context comparable.
- Language and culture: translate prompts using native or expert review; grammatical gender and local conventions can change the output.
- Nonbinary references: test them only where the system supports the wording, and record refusals or category collapse as results.
- Adversarial phrasing: vary adjectives, order, and contextual details to see whether a small wording change drives the effect.
- Refusal behavior: analyze who is refused, filtered, or rendered ambiguously; a refusal disparity is part of the user experience.
- Second dataset: use a fresh prompt set or independent corpus to test whether the pattern persists outside the tuning sample.
The ICO recommends new-data testing, human review, and regular monitoring for drift. Review false positives, disagreements, and outputs that reviewers cannot classify confidently before deciding that a remediation worked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I report, remediate, and monitor the audit?
A useful report lets an outsider understand what was tested, reproduce the comparison, and see where uncertainty remains.
Publish the audit record
- Scope, intended use, geography, date, model and policy versions.
- Complete prompt matrix, generation settings, seed or randomization rule, sample frame, holdout design, and exclusions.
- Codebook, reviewer qualifications, independent-labeling procedure, adjudication rules, and inter-rater agreement.
- Benchmark source, year, geography, collection method, category mapping, and uncertainty.
- Counts, rates, disparity measures, intervals, unknowns, refusals, sparse cells, and intersectional results.
- Known limitations, visual-inference risks, data-protection controls, and a change log.
Choose a remediation path
If disparities are material for the intended use, consider prompt-template changes, safer defaults, additional or better-balanced training data, model or policy changes, or escalation to an independent fairness audit. Do not “fix” a result by deleting inconvenient outputs or redefining categories after the fact. Re-test on the untouched holdout and a fresh run before deployment.
Set monitoring triggers
Rerun after a model release, safety-policy change, prompt-template revision, post-processing change, or shift in the target population. Preserve the original test set so trend comparisons remain possible, and add new cases when real-world incidents expose a blind spot.
How do model-to-model and model-to-human comparisons differ?
Use the same matrix and settings for model-to-model comparisons, but treat human-media comparisons as a corpus study rather than a simple scorecard.
| Comparison | Method | Main risk |
|---|---|---|
| Model to model | Run identical prompts, settings, sample size, labels, and benchmark for each version. | Interface defaults, safety policies, or hidden model updates can break comparability. |
| Model to human media | Document corpus sampling, genre, period, geography, casting, and labeling before comparing. | Differences may reflect genre or editorial selection rather than model bias. |
| Model to target population | Use current demographic or labor data that match the deployment geography and purpose. | Population categories may not map cleanly to visual labels or prompt roles. |
| Quick internal check versus formal audit | Use a quick check for directional signals; use preregistration, independent labels, holdout data, uncertainty, and reproducibility artifacts for a formal claim. | A small convenience sample can produce unstable or non-generalizable results. |
What are the most common audit mistakes?
Most failures come from design shortcuts rather than arithmetic.
- Auditing one attractive image: one output is an anecdote, not an estimate.
- Changing prompts until the result looks fair: this creates selection bias; freeze the matrix first.
- Inferring identity from appearance: use unknown and not-visible labels and explain the proxy nature of visual coding.
- Reporting only aggregate averages: intersectional harms disappear; break out role, race or ethnicity, age, disability, culture, and other relevant dimensions.
- Using an undocumented benchmark: a percentage without year, geography, population, or collection method cannot be interpreted.
- Dropping refusals and failures: filtering behavior can itself be discriminatory and must be counted.
- Ignoring presentation: equal headcounts can coexist with unequal agency, framing, styling, or sexualization.
- Overinterpreting small cells: show intervals, counts, and sparse-cell warnings.
- Failing to rerun after updates: model weights, safety filters, and interfaces change over time.
What transparency and legal checks belong in the workflow?
Include provenance and disclosure checks when the media will be published or used in a regulated setting, but do not assume one rule applies everywhere. The European Commission AI Act Service Desk states that Article 50 transparency obligations for AI-generated content apply from 2 August 2026; applicability depends on jurisdiction, the actor’s role as provider or deployer, and the content category. Verify the current legal text for the deployment location and record the legal interpretation used.
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The Australian Government AI Technical Standard, Statement 19, calls for measuring dataset representativeness, separating training from validation and testing data, managing demographic and intersectional bias, and documenting data use and bias incidents. Those controls complement, rather than replace, the empirical generation and review protocol.
What should be on a practical audit checklist?
- Define system, version, geography, date, audience, media unit, and constructs.
- Freeze a balanced prompt matrix with role, neutral, counter-stereotypical, and relevant intersectional cells.
- Predeclare generations per cell, seed or randomization, settings, holdout, and exclusion rules.
- Generate every cell, preserve originals and metadata, and count refusals and failures.
- Freeze a codebook with identity, role, agency, presentation, unknown, and not-applicable options.
- Use independent reviewers, lived-experience expertise, adjudication, and agreement statistics.
- Select and document a matching benchmark with year, geography, population, and uncertainty.
- Report counts, rates, disparities, intervals, missingness, refusals, and intersections.
- Stress-test with edge cases, languages, counter-stereotypes, fresh data, and adversarial phrasing.
- Publish limitations, remediate material disparities, preserve a holdout, and schedule reruns after changes.
Frequently Asked Questions
Can I audit a hosted image generator if I cannot see its model version?
Yes, but label the result as an interface-level audit rather than a model-level conclusion. Record the product name, access route, visible settings, date, region, account tier, and any release information shown to you; rerun periodically because an undisclosed backend change can alter results.
How many images should I generate for each prompt?
There is no universal number: choose a sample size that gives useful uncertainty for the smallest intersection you intend to report. Predeclare the number, show counts and intervals, and expand sparse cells instead of treating an unstable percentage as a finding.
Should refusals be counted as gender bias?
Count refusals and filtered generations as a separate outcome, then compare refusal rates across prompt cells. A refusal may reflect safety policy rather than visual representation, so interpret it as a pipeline behavior and investigate its effect on users.
How do I audit gender in generated video rather than still images?
Define a frame or scene-sampling rule before viewing the video, retain temporal context, and code who acts, speaks, leads, or remains visible over time. Report both frame-level presentation and scene-level agency so a single transient frame does not determine the conclusion.
When should an internal audit become an independent fairness review?
Escalate when disparities affect a high-impact use, persist across fresh data, involve vulnerable or intersectional groups, or cannot be resolved by clarifying the codebook. An independent review can challenge prompt design, benchmark choice, labeling assumptions, and remediation evidence.
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