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If an AI system appears to have treated you unfairly, preserve the original interaction before refreshing, editing or deleting anything. Save the exact inputs and outputs with timestamps, system and model details, settings, account or workflow context, and screenshots or exported logs. Then document the real-world decision and harm, test carefully chosen comparator cases, keep a dated chronology, request human review and preservation of logs, and report through the channel that matches the sector and jurisdiction. A screenshot by itself is rarely enough to establish what happened or why.
Freeze the original interaction first
Evidence is strongest when another person can reproduce or independently verify the event. Do not rely on memory or a cropped screenshot. Preserve the interaction before changing prompts, accepting a revised decision, clearing browser data or allowing a retention period to expire.
Capture the complete record
- Save the exact input. Copy every prompt, form answer, uploaded document, profile field and other value supplied to the system. Preserve spelling, capitalization, formatting and attachments. If the system used data you did not enter directly, note that fact and record what the interface disclosed about its source.
- Save the complete output. Keep the full response, score, ranking, recommendation, rejection notice, moderation action or other result. Include surrounding warnings, explanations, confidence indicators and appeal instructions; do not save only the sentence that seems offensive.
- Record time and context. Write the date and time in UTC and your local time zone. Identify the account, transaction, application, case or workflow in which the output appeared, and note the system and provider name.
- Record technical details. Copy the model name and version when shown, interface or API version, relevant settings, temperature or similar controls, enabled tools, policy mode and any human approval step. If a detail is not displayed, say that it was not displayed rather than guessing.
- Export and preserve files. Download available logs, decision letters and data exports. Take uncropped screenshots that show the address or application context, and keep the original files in read-only storage. When possible, calculate a cryptographic hash for each file and keep a record of who could access it and when a copy was made.
Use an evidence register
| Item | What to record | Why it matters |
|---|---|---|
| Input | Exact prompt, form values, documents and relevant profile data | Allows a reviewer to reproduce the system’s conditions |
| Output | Complete response, score, label, ranking or action | Shows what the system actually produced, not a paraphrase |
| Metadata | UTC and local time, system, provider, model/version, settings and workflow | Places the event in the correct technical and administrative context |
| Original files | Exports, logs and full-resolution screenshots kept read-only | Protects against accidental alteration and missing context |
| Access history | Who received a copy and every later change or annotation | Creates a basic chain of custody |
Keep an untouched original and work from a duplicate when adding highlights, translations or comments. Label every derived file as an analysis copy so it cannot be confused with the source.
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Explain what the system decided or recommended, what opportunity or service was affected, and the measurable consequence. Examples include a job application rejected, a credit score lowered, housing denied, an account removed, a benefit withheld or a person subjected to extra scrutiny. State the date, decision-maker if known, and any human action that followed the AI output.
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Separate facts from a discrimination hypothesis
Identify the protected characteristic that may be involved, such as disability, race, sex, age, religion or another characteristic recognized by the applicable law. If you do not know whether the system received that attribute, identify a plausible proxy and label it as a hypothesis until corroborated. A proxy might be a location, school, language pattern or employment history, but the presence of a correlation does not by itself prove unlawful discrimination.
The joint U.S. statement from the Equal Employment Opportunity Commission and Department of Justice explains that discriminatory outcomes can arise from unrepresentative or imbalanced data, historical bias, errors or correlations with protected classes. Your report should therefore distinguish the observed output from your explanation of its cause.
Build comparator evidence without exposing private data
A single result can show harm, but a pattern or a controlled comparison is usually needed to examine unequal treatment. Preserve outputs for people who are similarly situated or create controlled test cases in which the relevant input is held constant except for the characteristic or proxy being examined.
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Design a defensible comparison
- Define the decision. Specify the exact outcome being compared: eligibility, score, ranking, moderation status, price, screening or another action.
- Set inclusion rules before testing. Decide which cases count as comparable and record exclusions. Do not select only examples that support your theory.
- Change one relevant factor at a time. Keep qualifications, transaction details, wording and formatting constant where possible. Record every variation, including capitalization or order of fields.
- Repeat when the system is variable. Generative systems can produce different outputs for the same input. Record each run, the settings and the run time instead of presenting one run as a stable rule.
- Protect other people’s information. Do not copy another person’s private records into a report without authorization. Redact identifiers and, where possible, use synthetic or consented cases.
- Document limitations. State the sample size, selection method, missing data, changing system versions and any reason the comparison may not generalize.
NIST Special Publication 1270 (2022) frames bias work as a lifecycle: identify, understand, measure, manage and reduce harmful bias. A small set of carefully matched cases can reveal a lead, but it is not automatically a population-wide estimate.
Evidence qualities to evaluate
| Quality | Stronger practice | Common weakness |
|---|---|---|
| Reproducibility | Exact inputs, settings, version and repeated runs are preserved | A paraphrased prompt or one unrecorded run |
| Completeness | Full output, metadata, attachments and surrounding workflow are included | A cropped image with no date or context |
| Comparator quality | Similarly situated cases with pre-set selection rules | Different qualifications or hand-picked anecdotes |
| Privacy protection | Consent, minimization and redaction while originals remain secure | Unnecessary disclosure of another person’s sensitive data |
| Chain of custody | Read-only originals, hashes where possible and an access log | Files repeatedly edited without a history |
Keep a chronology and chain of custody
Create one dated log from the first output through every complaint and response. Include when you noticed the result, when you requested an explanation or correction, whom you contacted, what they said, and what remedy was offered or refused. Add witnesses, deadlines, subsequent decisions and any change in the system.
Store original evidence separately from interpretation. Number files consistently, for example by date and event, and note each transfer or annotation. If a representative or regulator receives a copy, record the recipient and date. This simple separation lets a reviewer see which statements are observed facts, which are your analysis and which questions remain unresolved.
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Ask for human review and preservation
Send a focused written request to the organization that deployed the system. Ask for a human decision-maker and for the organization to preserve relevant prompts, outputs, logs, model/version records and downstream decisions while the matter is investigated.
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- Identify the human who will review the decision and the deadline for that review.
- Explain whether and how the AI output influenced the decision, including any human override.
- Correct inaccurate personal data and rerun the decision using the corrected record.
- Provide an accommodation or alternative assessment where disability or accessibility is relevant.
- Pause, limit or monitor use of the system for your case while the issue is investigated.
- Preserve logs and related records so an appeal or regulator can evaluate the event.
- State the remedy sought: reconsideration, reinstatement, corrected score, restored access, refund, compensation or another concrete action.
Employment tools deserve particular care. The EEOC and DOJ have warned that such tools can screen out people with disabilities and have emphasized accommodation safeguards. EEOC Chair Charlotte A. Burrows said in 2022, “New technologies should not become new ways to discriminate.” In 2021 she also cautioned that these tools may “mask and perpetuate bias or create new discriminatory barriers to jobs.”
Choose the reporting route that fits the sector and jurisdiction
Start with the deployer’s appeal, privacy, ethics, human-resources, accessibility or complaint channel when doing so is safe. Then identify the regulator with authority over the decision, the protected right and the location where the harm occurred. A provider’s home country, your residence and the place of the transaction may all matter.
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| Situation | First escalation | Possible external authority | What to check |
|---|---|---|---|
| Employment screening, evaluation or accommodation | Employer HR, accessibility contact or formal appeal | Employment or civil-rights agency with jurisdiction | Filing deadline, retaliation protections, accommodation process and whether the employer or vendor holds the records |
| Consumer credit, insurance or other financial decision | Institution’s complaints and data-correction process | Consumer-finance or financial-services regulator | Adverse-action notice, correction rights, identity verification and the regulator’s complaint time limits |
| Housing, lending or public benefits | Provider’s appeal or fair-treatment channel | Housing, civil-rights or benefits authority | Jurisdiction, urgency of the loss and available interim relief |
| Privacy, profiling or data access | Organization’s privacy officer or data-protection contact | Data-protection or privacy regulator | Access, correction, deletion and objection rights, plus cross-border rules |
| General consumer deception or unsafe practice | Merchant or platform complaint channel | Consumer-protection authority | Whether the conduct affects many people and what evidence the authority accepts |
Confidentiality and remedies differ. Some portals disclose complaints to the organization, some investigate without promising an individual remedy, and some impose strict filing windows. Read the current instructions for the authority that covers your location and sector before sending sensitive material. Keep a copy of everything submitted.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.EU high-risk systems: understand Article 73 duties
For covered high-risk AI systems in the European Union, Article 73 of the AI Act places serious-incident reporting duties on providers to national market-surveillance authorities. The ordinary maximum is 15 days after the provider becomes aware of the incident. Specified widespread serious incidents have a two-day maximum, and an incident involving a death has a ten-day maximum. The consolidated text dated 27 July 2026 also requires investigation, risk assessment, corrective action and cooperation, and says changes that could compromise causal evaluation should be avoided before authorities are informed.
These rules do not automatically make every biased output an Article 73 incident, and an affected individual is not generally the party carrying the provider’s reporting duty. Preserve your evidence, notify the provider promptly and ask whether the event has been classified as a serious incident. Check the current consolidated text and national implementation guidance before relying on a deadline, because coverage, authority and procedure depend on the system and country.
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Assemble a report packet
A regulator, ombuds office or internal investigator should be able to understand the event without reconstructing it from scattered messages. Organize the packet in this order:
- Reporter and safe contact method: your identity, preferred contact channel and any safety or retaliation concern.
- Relationship to the decision: applicant, employee, customer, tenant, patient, student or another affected role.
- System and deployer: provider, organization using it, purpose, sector, geography and decision date.
- Technical record: exact inputs and outputs, screenshots, exported logs, model/version and configuration.
- Decision and harm: what happened, what opportunity or service was affected and the measurable consequence.
- Protected characteristic or proxy: identify the basis and mark any causal explanation as a hypothesis.
- Comparators: matched cases, test method, sample-selection rules and limitations, with unnecessary personal data removed.
- Chronology: output, complaints, responses, witnesses, changes and requested remedies in date order.
- Preservation and privacy notes: where originals are stored, who has access, what was redacted and why.
- Requested action: human review, accommodation, correction, rerun, restoration, explanation, monitoring or other remedy.
Plain-text report structure
Subject: Request for human review and preservation of AI decision records
Reporter and safe contact:
Affected role and decision:
Organization, system and purpose:
Decision date and location:
Observed inputs and complete output:
Technical details and attached files:
Concrete harm:
Protected characteristic or proxy (hypothesis if unconfirmed):
Comparator method and results:
Chronology of complaints and responses:
Records to preserve:
Requested remedy and response deadline:
Privacy or safety restrictions:Match your evidence to the kind of problem
Not every troubling output is the same allegation. Treating distinct situations alike can weaken a report.
| Situation | Most useful evidence | Likely next step |
|---|---|---|
| One offensive or demeaning response | Full prompt and output, time, account, settings and the impact on you | Report the content, request correction or removal, and ask what safeguard was triggered |
| One discriminatory decision | Decision notice, AI role, underlying data, human review request and harm documentation | Use the organization’s appeal and the sector authority that can order review or correction |
| Repeated unequal outcomes | Predefined comparators, repeated runs, version history and selection limitations | Escalate with a pattern analysis while protecting participants’ privacy |
| Urgent or irreversible harm | Immediate deadline, threatened loss, all preserved records and proof of urgency | Use an emergency appeal or legal or regulatory channel without waiting for a long study |
Common mistakes and how to avoid them
- Relying on a screenshot alone: add exact inputs, complete outputs, metadata and chronology.
- Editing the only copy: keep an untouched original and annotate a duplicate.
- Claiming intent too early: report the observed disparity and identify intent or causation as unresolved unless supported.
- Changing several variables at once: use matched cases and record every difference.
- Publishing someone else’s sensitive information: obtain authorization or redact and minimize before sharing.
- Ignoring human involvement: ask who reviewed, accepted or overrode the output; the AI may have been advisory rather than decisive.
- Missing a deadline: note the decision date immediately and verify the current filing period for the relevant authority.
- Assuming an EU incident deadline applies automatically: Article 73 covers specified serious incidents involving covered high-risk systems, not every biased response.
- Sending originals through insecure channels: use the organization’s protected portal when available and retain a secure copy.
How to make a credible claim without overstating it
Use three layers in every explanation. First, list observed facts: the exact output, the decision, the dates and the harm. Second, explain your analysis: the comparator method, apparent disparity and possible proxy. Third, identify unresolved questions: which data the system used, whether a human relied on it, and whether the behavior changed across versions. This structure makes a report useful even when the cause cannot yet be proven.
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There is no general prevalence or success-rate statistic that can predict whether a discriminatory-AI complaint will succeed. Outcomes depend on the sector, jurisdiction, evidence, deadlines, system design and available remedy. A careful, reproducible packet gives the receiving organization or authority the information needed to investigate those specifics.
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