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Document the complete decision trail, not just a troubling screenshot. Save the exact input and output, timestamps, system and model details, settings, account context, and any logs; describe the concrete harm; test the result against carefully selected comparators; preserve originals and a chain of custody; request human review and correction; then report through the organization’s complaint channel and the regulator responsible for the sector and jurisdiction. A single offensive response needs a different escalation strategy from a repeatable pattern that denies jobs, housing, credit, health care, education, or another opportunity.
What makes an AI output discriminatory?
An offensive or inaccurate answer is not automatically proof of unlawful discrimination. The report becomes stronger when you connect the system’s output to a real decision or service, identify a protected characteristic or a plausible proxy, and show that similarly situated people were treated differently or that a policy produced a disproportionate harmful effect.
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Possible causes include unrepresentative or imbalanced training data, historical bias, model or implementation errors, and correlations with protected classes. The 2022 U.S. joint statement from the Equal Employment Opportunity Commission (EEOC), Department of Justice, Consumer Financial Protection Bureau, and Federal Trade Commission recognizes these pathways. Treat a suspected proxy as a hypothesis until the evidence supports it; do not present an inference as an established fact.
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| Pattern | What to preserve | What it can establish |
|---|---|---|
| One offensive or stereotyped response | Verbatim prompt, full response, date, account and configuration context, and whether the response was generated again | A potentially harmful output and a reason for safety, quality, or policy review; by itself it may not prove a discriminatory decision |
| A discriminatory decision or repeated disparity | All of the above, plus the decision record, affected opportunity, comparator cases, sample-selection method, and chronology of complaints | A basis for human review, correction, accommodation, internal investigation, or a regulator complaint |
Preserve the original interaction before it changes
Interfaces can edit, truncate, regenerate, or delete conversations. Capture the evidence while the interaction and surrounding account state are still available.
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- Save the exact input. Copy the complete prompt, uploaded document, form values, images, audio, code, or API request. Record whether the input was typed, imported, transformed, or prefilled.
- Save the complete output. Export the conversation or response where possible. Take screenshots that include the surrounding interface, not only the sentence that appears biased. If the system provides a confidence score, explanation, rank, label, or rejection reason, preserve each one.
- Record time and context. Write the local time and UTC time, date, account or workflow identifier, organization using the system, purpose of the workflow, and the user’s role. Note the interface, language, region, accessibility settings, and any human review shown on screen.
- Identify the system. Record the provider, deployed product, model name and version if displayed, release or build identifier, API version, and relevant system instructions or policy settings that you are authorized to see. If a model version is not shown, record that it was not disclosed.
- Preserve files and metadata. Keep the original export or download in read-only storage. Make a working copy for redaction or analysis. When possible, calculate a cryptographic hash and record the algorithm, hash value, file name, and creation time.
- Maintain an access log. List who received, copied, opened, or edited each file. Record every subsequent change in a separate analysis log; never overwrite the original.
A screenshot alone is weak because it may omit the prompt, model version, settings, surrounding decision, or time. Pair it with reproducible inputs, the unedited output, metadata, and an explanation of where the output was used.
Describe the decision and measurable harm
State what the system did and what happened to a person as a result. Use observable facts before interpretation.
- Decision: the system rejected an application, assigned a lower score, removed content, routed a person for extra scrutiny, denied access, recommended a disciplinary action, or generated a classification used by a human.
- Opportunity or service: employment, promotion, housing, lending, insurance, education, health care, public benefits, account access, safety screening, or another identified service.
- Consequence: a missed interview, lost income, higher price, denial, delay, removal, lost accommodation, reputational injury, or other measurable effect. Include dates, amounts, and documents when available.
- Protected characteristic or proxy: identify the characteristic implicated by the evidence, such as disability, race, sex, age, religion, national origin, or another category recognized in the applicable jurisdiction. If you suspect a proxy, describe the data feature and label the connection as unconfirmed.
- Human involvement: say whether a person reviewed, overrode, adopted, or merely received the AI result. Do not assume a human review cured the problem without evidence of an independent assessment.
Keep three layers separate: observed facts, your analysis of why they may indicate discrimination, and unresolved hypotheses. This separation helps an investigator test your claim rather than dismissing a conclusion that cannot be reproduced.
Build comparator evidence without violating privacy
Comparator evidence shows whether the system changes its result when the relevant characteristic changes while other material inputs remain constant. The goal is a fair, documented comparison—not disclosure of another person’s private information.
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Use controlled tests where you are authorized
- Define the decision being tested and the population to which the result is supposed to apply.
- Write a sample-selection rule before choosing cases. Record inclusion criteria, exclusions, sample size, date range, and any missing data.
- Keep qualifications, experience, transaction facts, language, formatting, and other material inputs constant. Change only the characteristic or proxy under examination, and only in a lawful, authorized test environment.
- Run each case under the same model version, settings, temperature or randomness controls, interface, and time window when possible. Repeat stochastic tests and record every run rather than selecting only the worst result.
- Store outputs with a case identifier, not another person’s name or unnecessary personal data. Obtain authorization before using someone else’s records.
- Report limitations: small samples, synthetic cases, confounding variables, changing model versions, and results that cannot be generalized.
NIST Special Publication 1270 (2022) recommends lifecycle work to identify, understand, measure, manage, and reduce harmful bias. A comparator set is one part of that process; it is not, by itself, a legal finding.
What makes a comparator credible?
| Factor | Stronger practice | Weak practice |
|---|---|---|
| Similarity | Materially equivalent qualifications, transaction facts, or case details | Different facts that make the outcomes incomparable |
| Isolation | Only the characteristic or defined proxy changes | Several inputs change at once |
| Reproducibility | Same version, settings, workflow, and documented reruns | One undocumented prompt or a regenerated answer selected after the fact |
| Privacy | Consent, authorization, redaction, and case identifiers | Publishing another person’s private records |
| Limitations | Sampling rules, missing data, and uncertainty stated plainly | Presenting a small or synthetic test as proof of population-wide impact |
Create a chronology and chain of custody
Make a dated timeline from the first output through every complaint and response. Include when you discovered the result, when a decision took effect, when you requested records or accommodation, who replied, what they said, and what remedy was offered or refused.
For each evidence item, record an identifier, source, collection date and time, file type, hash if available, original location, authorized collector, access history, and any transformation. Keep originals separate from redacted copies, screenshots, transcripts, statistical calculations, and explanatory notes. Label derived files as analysis so an investigator can distinguish them from what the system actually produced.
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Send enough information for the recipient to locate the event without distributing unnecessary sensitive data.
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| Section | Include |
|---|---|
| Reporter and affected person | Identity, safe contact method, relationship to the decision, and any safety or confidentiality concern |
| System and deployment | Provider, deployer, purpose, model or version if known, interface or API, sector, geography, and decision date |
| Interaction record | Exact inputs and outputs, screenshots, exports, logs, configuration, timestamps in UTC and local time, and file identifiers |
| Impact | Decision, affected opportunity or service, measurable harm, protected characteristic or suspected proxy, and human involvement |
| Comparators | Case identifiers, matching method, outputs, sample-selection rules, authorization, and limitations |
| Chronology | Output, complaint, responses, preservation requests, and remediation offered |
| Privacy controls | Redaction and minimization decisions, secure transfer method, access list, and location of originals |
| Requested action | Human review, preservation of logs, explanation of the AI’s role, data correction, rerun or accommodation, pause or limitation of use, and written response |
Request human review and a remedy
Send a focused request to the organization that made or deployed the decision. Ask it to identify a human decision-maker and preserve the relevant prompts, outputs, model version, audit logs, reviewer notes, and input data. Ask what role the AI result played and whether a person independently verified it.
Request correction of inaccurate data, a new decision without the disputed factor where appropriate, and an accommodation when a disability or accessibility issue is involved. The EEOC and Department of Justice have warned that employment tools can screen out people with disabilities and emphasized safeguards for accommodation. The EEOC has stated: “New technologies should not become new ways to discriminate.” It has also warned that tools may “mask and perpetuate bias or create new discriminatory barriers to jobs.”
Ask the organization to limit or pause use of the output while it investigates if continued use could cause additional harm. Set a reasonable response date, retain proof of delivery, and continue preserving evidence even if the organization promises an informal fix.
Choose the right reporting route
Begin with the deployer’s appeal, privacy, ethics, human-resources, accessibility, or complaint channel when doing so is safe. Then select the external authority that matches the sector and location. A complaint to the wrong office can delay urgent relief, so verify the current portal, jurisdiction, filing period, and confidentiality rules before submitting.
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| Situation | Likely route | Questions to check |
|---|---|---|
| Hiring, promotion, firing, workplace screening, or disability accommodation | Employment or civil-rights enforcement body, plus the employer’s HR or accessibility process | Who is covered, whether an administrative charge is required, filing deadline, interim relief, and accommodation procedure |
| Credit, lending, insurance, or financial scoring | Financial regulator or consumer-protection authority, and the provider’s dispute process | Notice and explanation rights, correction process, jurisdiction, deadline, and available restitution |
| Housing or tenant screening | Housing or civil-rights authority and the housing provider’s appeal channel | Protected classes, adverse-action notice requirements, local deadline, and anti-retaliation protection |
| Privacy, profiling, or automated access decisions | Data-protection or privacy regulator, where applicable | Access, correction, objection, human-intervention rights, identity verification, and cross-border jurisdiction |
| Consumer-facing deceptive or unfair AI claims | Consumer-protection authority | Whether the conduct is within the agency’s remit and what evidence or injury it requires |
| Covered high-risk AI in the European Union | National market-surveillance authority through the provider’s serious-incident process | Whether the system and event fall within Article 73, who has the reporting duty, and the applicable deadline |
There is no universal success rate or prevalence figure for reports of discriminatory AI outputs. Legal coverage, regulator powers, remedies, confidentiality, and deadlines vary by country, sector, and date.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.EU high-risk systems: Article 73 timing and preservation
For covered high-risk AI systems, Article 73 of the EU AI Act requires providers to report serious incidents to the relevant national market-surveillance authority. The consolidated text dated July 27, 2026 sets an ordinary maximum of 15 days after the provider becomes aware of the incident. The maximum is two days for specified widespread serious incidents and ten days when a death is involved.
The rule also requires investigation, risk assessment, corrective action, and cooperation with authorities. Providers should avoid changes that could compromise causal evaluation before authorities are informed. These duties do not turn every biased answer into an Article 73 incident: coverage depends on the system’s legal classification and the seriousness and consequences of the event. Check the current consolidated text and national implementation guidance before relying on a deadline or assuming that a complainant, rather than the provider, carries the reporting duty.
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- Send the minimum personal data needed to identify and investigate the event. Redact unrelated account numbers, health details, passwords, private messages, and other people’s identifiers.
- Keep an unredacted original in secure, access-controlled storage. Send a redacted copy and explain that the original is preserved and available through a secure process.
- Use the recipient’s secure upload or encrypted-transfer method. Do not place sensitive evidence in a public post merely to obtain attention.
- Ask how confidentiality, retaliation, and data retention are handled. A regulator may have limits on anonymous complaints or on withholding identity from the organization.
- If the decision affects immediate safety, housing, income, health care, immigration status, or access to essential services, seek time-sensitive legal or emergency assistance in parallel with the technical complaint.
Common failure modes and how to fix them
Only a cropped screenshot was saved
Request the complete export, reconstruct the prompt and workflow from contemporaneous records, and document what is missing. Do not imply that unseen metadata exists.
The model changed before testing
Preserve the old output and version information, ask the deployer to retain historical logs, and label later tests as a different version. Do not combine results from incompatible versions without explaining the change.
The comparison changed too many variables
Redesign the test with a written matching rule, controlled inputs, repeated runs, and a privacy review. Keep the initial flawed test as an analysis file rather than deleting it.
The organization says a human made the decision
Ask for the AI’s role, the human reviewer’s criteria, the records considered, and whether the reviewer could override the output. A human label does not answer whether the tool influenced the result.
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Replace names and identifiers with case codes, obtain authorization, and explain the redaction. Preserve the original securely instead of attaching it broadly.
Quick Recap
Use this final submission checklist
- Exact input, complete output, screenshots, exports, and logs saved.
- UTC and local timestamps, system, provider, model/version, settings, account, workflow, and geography recorded.
- Decision, affected opportunity, measurable harm, characteristic or proxy, and human role described separately from hypotheses.
- Comparator method, authorization, privacy safeguards, sample limits, and repeated results documented.
- Originals hashed or otherwise protected, read-only copy retained, and access history recorded.
- Chronology includes complaints, responses, preservation requests, and proposed remedies.
- Human review, correction, accommodation, rerun, log preservation, and pause or limitation requested.
- Sector, jurisdiction, filing period, confidentiality terms, and reporting duty checked before escalation.
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