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If an AI product appears to treat people differently because of gender, create a precise, reproducible record before changing anything. Save the product and version, date and time, exact inputs, unedited outputs, settings, and the real-world consequence. Report the incident through the provider’s official support or complaints channel, request a case number and written response, and consider a regulator or employment-discrimination route when the system affected a hiring or workplace decision. A troubling output justifies documentation and review; it is not, by itself, proof that unlawful discrimination occurred.
What to do first
Work in this order so that a later investigation can distinguish what the system produced from what you remember about it.
- Stop and preserve the state. Do not overwrite the chat, project, file, or account session that contains the result. If the product may rotate logs or expire sessions, export the conversation or download the relevant files immediately.
- Record the identity of the system. Write down the product name, model or version shown in the interface, account or workspace, plan or deployment (if displayed), and any plug-ins, retrieval sources, or connected tools that were active.
- Capture the complete interaction. Save the exact prompt, uploaded material, system instructions you were allowed to see, settings, safety notices, warnings, explanations, and the full output. Take a screenshot of the relevant screen, but keep the underlying original file or export as well.
- Note the consequence. State what decision followed, who made it, when it happened, and how the output affected you or another person. Distinguish a recommendation from an actual decision.
- Make a separate chronology. Use a dated log for the first run, later tests, communications, decisions, and deadlines. Keep this chronology separate from the original evidence so that later notes cannot be mistaken for the product’s output.
- Preserve communications. Keep copies of support tickets, emails, notices, and attachments exactly as sent and received. Record ticket numbers and the name or role of each contact.
Do not edit a screenshot or output to make the concern clearer. Make a redacted copy for sharing, while retaining an unaltered original in a secure location.
When a difference may indicate gender bias
Different answers are a signal to investigate, not a legal conclusion. Outputs can change for many reasons, including wording, context, qualifications, data supplied, random variation, or a model update. The relevant question is whether a gender-related factor, or a proxy for it, appears to have changed treatment in a context where that difference caused harm.
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- Define the protected factor. Gender may be stated directly, expressed through pronouns or honorifics, or inferred from names and other details. Do not assume that a name is an accurate indicator of someone’s gender.
- Define the treatment. Record the concrete difference: a lower score, a refusal, a different salary recommendation, a harsher risk label, a changed ranking, or a different explanation.
- Define the impact. Identify whether the result merely appeared on screen or was used in hiring, promotion, access to a service, education, housing, healthcare, moderation, or another consequential process.
- Separate system behavior from human action. A person may have reviewed, overridden, or ignored the AI recommendation. Document each step instead of attributing the final decision to the model without evidence.
The Federal Trade Commission summarized the general risk this way in a June 2022 announcement: “AI tools can be inaccurate, biased, and discriminatory by design.” That statement is a warning about risk, not a finding about your particular product or incident.
Evidence to save: a practical record
The following fields make an incident understandable to someone who was not present. They are practical documentation choices, not a universal legal form.
| Record | What to capture | Why it matters |
|---|---|---|
| Product identity | Exact product name, model, version, vendor account, workspace, and deployment environment. | Behavior may differ between models, regions, editions, or later releases. |
| Date and time | Start and end times, including the time zone; note whether the system displayed a local or UTC time. | It anchors the event and helps match provider logs or a later model change. |
| Session context | Account type, permissions, browser or app, connected tools, retrieval sources, and relevant feature flags. | Two users can receive different behavior from different configurations. |
| Inputs | Exact prompts, form fields, uploaded documents, images, code, and any preceding conversation. | Small wording changes can alter an output; investigators need the original input. |
| Settings | Language, temperature or creativity control, filters, ranking mode, scoring rubric, and other visible options. | Settings can affect consistency and comparability. |
| Outputs | Complete, unedited responses, scores, labels, rankings, refusals, and explanations. | Partial quotations can hide qualifications or contradictory text. |
| Warnings and notices | Disclaimers, confidence indicators, safety messages, error codes, and notices about human review. | These may show how the product represented uncertainty or its intended use. |
| Human decision | Decision-maker, decision date, action taken, and documents that show how the AI result was used. | It connects a model output to an actual consequence. |
| Comparison runs | All paired or repeated tests, including runs that did not support your concern. | A complete set is more credible than selected examples. |
| Chronology | Every test, notice, appeal, support contact, response, and follow-up. | It prevents date confusion and exposes missed response or filing windows. |
Use a consistent file naming scheme such as 2026-10-16T1430Z_product_prompt01_output-original.txt. Keep a read-only copy and a working copy. If your organization has retention or legal-hold rules, follow them; do not destroy evidence merely because a case appears resolved.
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How to test the concern without weakening your evidence
Change one relevant factor at a time
Where practical, create matched inputs that are identical except for the factor you are examining. For example, keep qualifications, job history, wording, and formatting constant while changing a name or pronoun. Run the tests under the same account, settings, model, and time window. If a factor cannot be isolated, explain the confounder rather than presenting the comparison as controlled.
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Keep every result
Save favorable, unfavorable, inconsistent, and error results. Record run order and whether you repeated a test. A small number of examples can demonstrate a reproducible incident, but they do not establish a system-wide pattern on their own.
Use a safe test corpus
Prefer synthetic or authorized records. Do not upload another person’s sensitive information merely to probe a model, and do not publish names, medical details, employment files, or account identifiers that are unrelated to the issue. If real data is necessary, obtain appropriate authorization and minimize what you disclose.
Document model changes
Note any update notice, changed model label, altered policy, or unexplained behavior change between runs. A later result may not be comparable to the original if the provider changed the system.
Preserve originals and protect personal information
Keep the original export, download, email, or log in its native format. Screenshots are useful for showing what the interface displayed, but they can omit metadata or hidden context. Do not crop away timestamps, warnings, or surrounding fields that explain the decision. If you must annotate an image, work on a copy and label it as annotated.
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Before sending evidence outside the organization, redact unrelated personal information, authentication tokens, private messages, payment details, and other people’s data. Keep a private index that explains each redaction. Do not alter the substantive prompt or output. Share the minimum necessary material with each recipient and use a secure transfer method.
Maintain two records: an evidence set containing immutable originals and a case file containing your chronology, explanations, redacted copies, and correspondence. This separation makes it easier to show what the product actually produced.
How to report the incident to the product provider
- Open the product’s official support, feedback, safety, or complaints channel. Use the channel designated for the affected product rather than an unrelated social-media account.
- Give a short factual description, followed by the evidence details. Identify the product and version, date and time zone, account or session context, exact inputs and outputs, settings, and the concrete impact.
- Explain the comparison, if any. State what was held constant, what changed, how many runs you retained, and whether results were inconsistent.
- Ask the provider to preserve relevant logs and investigate the incident. Request an explanation of the factors used, whether a human reviewed the result, and whether corrective action or a re-review is available.
- Request a case number, the name or team responsible, expected response timing, and a written outcome. Save the complete submission and every attachment.
- If the outcome affects an imminent decision, say so and request a pause or human review, without assuming the provider has authority to stop the employer, school, agency, or other decision-maker.
A concise report can follow this structure:
Subject: Suspected gender-related difference in on [date]
Product and version:
Account or session:
Date/time and time zone:
Decision or use case affected:
What happened:
[Describe the output and the consequence without calling it illegal discrimination.]
Inputs and outputs:
[Attach or securely link the originals; list settings and warnings.]
Comparison:
[State the single changed factor, controls, number of runs, and exceptions.]
Requested action:
[Preserve logs, investigate, explain the result, and provide a case reference and written response.]
Contact and safe reply method:
Ask for confirmation that the provider received sensitive attachments. If it refuses to investigate, records the issue without a case number, or gives an unexplained answer, keep that response in your chronology; it may be relevant to a later regulator or employment inquiry.
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The U.S. Equal Employment Opportunity Commission (EEOC) says federal employment-discrimination laws protect people when AI is used to discriminate on protected grounds including sex and gender-related grounds. The possible claimant may be a job applicant, employee, or other person covered by the employment law at issue; the responsible party may be an employer, employment agency, or union.
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Start with the EEOC or a state or local agency
You can begin an inquiry through the EEOC Public Portal and schedule an intake interview. A state or local Fair Employment Practices Agency (FEPA) may also accept a claim. Work-sharing arrangements can allow a charge to be filed with both agencies, but the correct filing path depends on the jurisdiction and facts. Prepare the respondent’s identity, a short description of the allegedly discriminatory action, relevant dates, and a signed statement, along with your supporting records.
Watch the filing window
The EEOC states that the general deadline for a charge is 180 calendar days from the discriminatory event. It can extend to 300 days where a state or local agency enforces a law prohibiting discrimination on the same basis. Exceptions and special rules apply, including for some age claims. Federal employees and federal job applicants use a different process. An internal grievance, provider complaint, or other dispute forum generally does not extend the EEOC period, so contact the EEOC or a FEPA promptly when timing is uncertain.
Describe the employment decision, not only the model output
Explain who used the tool, what stage of hiring or employment it influenced, what human review occurred, and the action taken. Include comparable applicants or records only when you are authorized to share them. A difference in AI output alone does not establish a violation; the agency will assess the facts, covered law, and evidence.
Routes outside the United States
Choose the authority based on the country, sector, and harm. A national equality body, data-protection authority, labor inspectorate, consumer regulator, ombuds office, or sector-specific regulator may have jurisdiction. Check its current intake rules, language requirements, evidence limits, and deadlines. A provider complaint can help create a record, but do not assume it pauses a statutory filing period.
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European Union context
The European Commission’s 2021 report Algorithmic discrimination in Europe examines how algorithmic discrimination interacts with EU gender-equality and non-discrimination law. It is useful legal context, but it does not identify one complaint office for every member state, product, or use case.
The Commission published provider and deployer transparency guidelines on 20 July 2026 and states that Article 50 transparency obligations apply from 2 August 2026. The AI Act Service Desk’s Article 4a material, based on a consolidated text dated 27 July 2026, describes narrowly conditioned processing of special-category personal data for bias detection and correction, with safeguards. These provisions concern system obligations; they are not a universal individual reporting channel and do not create blanket permission to collect or disclose sensitive data for testing.
Choose the route that matches the harm
| Route | Best fit | What it can do | Important limit |
|---|---|---|---|
| Product provider | You need logs, a technical investigation, correction, or a re-review. | Controls the system and may explain or remediate product behavior. | It may not control the employer’s or agency’s final decision and is not a substitute for a statutory filing. |
| EEOC | A covered U.S. employment decision may involve sex or gender discrimination. | Receives and investigates charges under federal employment law and may facilitate resolution. | Eligibility, deadlines, and procedure depend on facts; federal employees and applicants follow a separate process. |
| State or local FEPA | The work occurred in a jurisdiction with its own employment-protection law. | May investigate under state or local law and coordinate with the EEOC. | Jurisdiction, filing rules, and time limits vary. |
| National or sector regulator | The incident occurred outside U.S. employment or in a regulated sector. | May supervise organizations, investigate compliance, or collect complaints. | Authority and remedies differ by country and sector; identify the competent body first. |
When more than one route is plausible, list them in your chronology, verify each deadline, and ask the relevant office whether filing with another body affects its process. Do not wait for a provider’s internal investigation if a legal deadline is approaching.
Quick Recap
Common mistakes that weaken a report
- Editing the record. Rewriting a prompt, cropping away a warning, or saving only a preferred output makes independent verification harder.
- Overstating the conclusion. Say “the output differed under these conditions” or “I suspect gender-related bias,” then provide the evidence. Do not label the conduct illegal solely because two outputs differ.
- Testing with uncontrolled variables. Changing the prompt, account, model, and data at once prevents a meaningful comparison.
- Cherry-picking. Include contradictory and failed runs, not only examples that support your concern.
- Exposing other people. Do not publish or send unnecessary names, protected records, or credentials. Redact copies and retain originals securely.
- Assuming the vendor is the only responsible party. Identify the organization that made or implemented the consequential decision and document the human review chain.
- Missing a deadline. A support ticket or internal appeal generally does not preserve an EEOC or other statutory filing period.
Final submission checklist
- Exact product, model or version, account context, and deployment are identified.
- Every date and time includes a time zone.
- Original prompts, uploads, settings, warnings, and outputs are preserved.
- The concrete decision, consequence, and human review are described.
- Comparison runs change one relevant factor where practical and include unfavorable results.
- A separate chronology lists tests, decisions, contacts, and deadlines.
- Copies sent to others remove unrelated personal information without altering the substance.
- The provider report requests log preservation, investigation, explanation, case reference, and written response.
- The appropriate employment, equality, consumer, data, or sector authority has been identified for the location and context.
- Any filing deadline has been checked independently; the provider complaint is not treated as a deadline extension.
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