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An AI-generated crime tip is a lead to verify, not a finding of fact. Whether an AI system wrote a report, summarized a message, ranked a suspect, or flagged a video, do not treat its wording, confidence score, or apparent certainty as proof that a crime occurred or that a person is responsible. First assess immediate danger, preserve the original material, check specific claims through independent channels, and involve a qualified human reviewer before consequential action.
The process below is based primarily on U.S. federal guidance. The FBI describes its own AI output as investigative leads that trained investigators or analysts assess before substantive action. The National Institute of Standards and Technology (NIST) calls for task-specific testing on representative, post-processed evidence, while the U.S. Department of Justice (DOJ) says AI output should not alone support forensic conclusions. Local law, agency policy, and laboratory procedures may impose additional requirements.
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The rule that should govern every AI-generated tip
Start by separating three different things that are often blended together:
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- Source material: what a person actually submitted, what a camera recorded, or what an account posted.
- AI interpretation: a transcription, summary, classification, generated description, risk score, or suggested connection.
- Investigative conclusion: a human determination, supported by verified facts and appropriate procedure.
An AI system can help organize information or identify a lead. It cannot, by itself, establish that an event happened, authenticate a file, identify a person, or prove criminal intent. The FBI states that information produced by its own AI techniques is used for investigative leads and that trained investigators or analysts assess the output before substantive action. It also says, “Our investigations require an extremely high degree of certainty, and we currently verify and validate all SI-generated leads with human experts.” The agency summarizes accountability this way: “A human being is ultimately accountable for the actions taken, not an SI.”
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That is a practical standard for anyone handling a tip: use automation to decide what deserves examination, never to skip examination.
A seven-step workflow for handling a tip safely
1. Triage immediate danger before checking whether the story is true
If the message suggests an imminent threat to life, an active attack, a missing person in immediate danger, or another emergency, contact the appropriate emergency service in your jurisdiction. Do not wait for a detector, an AI explanation, or a complete evidence package. The FBI’s tip guidance specifically says its tip form is not for emergencies; emergency and non-emergency channels are different.
For a non-emergency report, slow down enough to avoid amplifying an unverified allegation. Identify the jurisdiction and the agency that can actually respond. A local police department, a national reporting center, a platform, and an employer may have different roles. If you are unsure, use contact information found through an official site or known telephone number rather than a link in the message itself.
2. Write down the claim exactly as received
Create a contemporaneous record before paraphrasing. Note the precise allegation, the person or organization mentioned, the alleged date and time, the location, and what the source says happened. Mark each statement as one of the following:
- Directly observed or supplied: for example, a sender provides a video file or says they were present.
- AI-generated description: for example, a system labels a figure as a particular person or calls an action a robbery.
- Your inference: an explanation you are considering but have not verified.
Keep the receipt time and the identity or account information available to you. If the report concerns online activity, the FBI advises providing concrete details such as the site URL, application, username, and date and time. Submit a non-emergency tip only once through the appropriate channel; multiple copies can create confusion and make it harder to determine which version is original.
3. Preserve the original and its context
Retain the source message, original file or post, surrounding conversation, account or profile details, and any visible time information when doing so is lawful and safe. Save the material in a way that does not overwrite the only copy. Do not rely solely on an AI-generated transcript, screenshot, thumbnail, or summary when the underlying file or page is available.
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Opening or downloading material can create security and privacy risks. Do not log in to someone else’s account, bypass access controls, or expose private information just to investigate. If the allegation could lead to arrest, employment action, public accusation, or another serious consequence, use trained forensic handling rather than repeatedly re-encoding, enhancing, or editing the only copy. NIST’s digital-investigation guidance emphasizes that digital evidence has practical limits and that handling choices affect what can later be examined.
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4. Verify identities and official instructions through an independent channel
AI-assisted scams often combine a plausible allegation with an impersonation of law enforcement, a court, or a public official. The FBI has warned about synthetic videos and spoofed reporting sites. Treat an unexpected message that demands payment, credentials, urgent secrecy, or a particular download as potentially fraudulent even if the attached voice or video appears familiar.
Do not click a reporting link supplied in an unsolicited message and assume it is official. Instead, type the known official IC3 address directly, or navigate from a government site you reached independently, and verify the domain before submitting information. Call an agency using a number published on its official site or on a bill, badge, or document you already trust. Do not use a phone number or QR code that appears only in the suspicious message.
Identity verification has two separate parts: confirming that a communication really came from the claimed organization, and confirming that the allegation inside it is true. A genuine agency account can transmit an incorrect tip; a forged account can transmit a real-looking file. Neither question answers the other.
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Test the important details against sources that did not simply copy the AI output. Depending on the allegation, that may include a direct record, a separate witness, an access log, a contemporaneous message, a public filing, or footage from an independent system. Corroboration should address the claim itself, not just the fact that several accounts repeat the same generated summary.
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Ask targeted questions:
- Does the stated time fit an independently recorded event or travel record?
- Can the location be confirmed without relying on the tip’s caption?
- Is the named person’s identity established by a reliable source, or only by an AI label?
- Is there an alternative explanation that fits the same file or behavior?
- Are multiple sources genuinely independent, or did they all receive the same original post?
Absence of corroboration is not proof that a report is false. It means the claim remains unverified and should be described that way.
6. Examine media for anomalies without turning clues into a verdict
Unnatural motion, inconsistent lighting, distorted facial details, lip-sync problems, abrupt changes in texture, mismatched reflections, or unusual audio timing can justify closer review. They are not a conclusive authentication procedure. The FBI presents such features as signs to look for, not as a test that proves a video is genuine or fabricated.
Compression, cropping, screen recording, re-encoding, noise reduction, subtitles, and platform processing can create the same apparent defects. Conversely, a sophisticated synthetic file may not display obvious anomalies. Record what you observe and how the file was obtained, but do not convert a visual impression into “the suspect is present” or “the video is fake.”
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A detector’s label also needs a narrowly defined meaning. “Manipulated” might refer to a face swap, an edited soundtrack, a changed segment, or a file whose provenance cannot be established. “Authentic” may mean only that the tested feature did not trigger that particular model under those conditions. Ask what the tool actually evaluated before relying on its output.
7. Document uncertainty, review, and escalation
Before a consequential decision, create a short case note that distinguishes:
- facts directly supported by preserved material;
- claims supported by independent sources;
- AI-generated interpretations that remain unverified;
- alternative explanations still open;
- tests or searches that were performed and their conditions;
- the name or role of the human reviewer; and
- the next action, responsible authority, and reason for escalation.
Route a substantiated allegation through the relevant official channel. Do not publicly name, threaten, contact, or confront a suspected person because an AI system produced a compelling output. Public exposure can endanger people, contaminate witness accounts, destroy evidence, and create legal risk. A reviewer should be able to explain not only why a lead was escalated, but also what remains unknown.
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How to judge an AI or detection tool before relying on it
The right question is not “How accurate is this AI?” in the abstract. It is “Accurate for which task, on which evidence, under which conditions, and with what consequences when it is wrong?” NIST’s forensic evaluation work emphasizes targeted questions, representative evidence, testing on post-processed material, and continuing reassessment as generation methods change. DOJ guidance adds intended use, training data and methods, validation, limitations, potential bias, demonstrated accuracy, and human interpretation.
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|---|---|---|
| Task fit | Is the tool testing authenticity, identifying a person, locating a manipulated segment, or assessing provenance? Does that match the decision you need to make? | A single score is presented as an answer to several different questions. |
| Evidence conditions | Was it tested on the same medium, language, resolution, compression, camera type, and post-processing found in the tip? | Marketing examples use pristine files while real inputs are screen recordings or heavily compressed clips. |
| Validation and updates | Who independently validated it, when, with what test set, and how are model updates documented? | No dated validation record or no explanation of how performance changes after an update. |
| Error behavior | What kinds of false positives and false negatives occur, and how are borderline results reported? | Only a confidence percentage is shown, with no error analysis or threshold rationale. |
| Interpretability | Can a qualified reviewer see which frames, features, or records influenced the result and reproduce the analysis? | An opaque label is treated as self-explanatory evidence. |
| Human governance | Who reviews the output, what training do they have, and is there a documented process for disagreement or appeal? | The tool result automatically triggers an arrest, takedown, search, or public accusation. |
A tool that performs well on one question may be unsuitable for another. A system designed to find likely face replacement cannot establish who supplied the file, whether an event occurred, or whether a person intended a crime. NIST describes forensic evaluation as scenario-specific and ongoing; treat a published result as bounded evidence, not a permanent guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why confidence scores and fluent reports mislead
Generative systems can produce a polished narrative even when the input is incomplete, ambiguous, or wrong. A confidence score may describe the model’s internal ranking rather than a verified probability that a crime occurred. Unless the provider defines the score, validates its calibration for the relevant population and task, and explains uncertainty, it should not be used as a legal or investigative threshold.
Ask the system to expose uncertainty rather than forcing a binary answer. Preserve the original output, including warnings and rejected alternatives, but label it as machine-generated. A human reviewer should check the source material independently instead of editing the AI narrative until it sounds authoritative.
Handling common tip formats
AI-written text or a chatbot summary
Style alone cannot reliably tell you whether a person or a model wrote a message. Examine the underlying facts, source identity, timing, and corroboration. A detailed narrative can still be fabricated, and a genuine witness may write briefly or inconsistently. Ask the sender for original records through a safe channel and verify key details independently.
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Voice likeness is not identity proof. Check whether the clip is an original recording, an excerpt, or a repost; whether the channel may have altered it; and whether the speaker’s words are supported by records or witnesses. Do not call back using a number supplied only in the clip or accompanying message.
Video or still images
Keep the highest-quality lawful original and its surrounding context. Note visible anomalies, but account for ordinary editing and platform compression. A detector result should answer a defined question and be reviewed by someone qualified to interpret its limitations.
Prioritized lists or risk scores
A ranking can help allocate attention, but it is not a finding about the people on the list. Check what data produced the ranking, whether the data may encode bias or incomplete reporting, and what human review is required before any contact, search, surveillance, or other intervention.
When a professional forensic review is warranted
Escalate to a qualified forensic examiner or appropriately trained investigative unit when the material could support criminal proceedings, determine someone’s liberty, affect a vulnerable person’s safety, or create a significant employment or reputational consequence. Provide the preserved original, acquisition notes, relevant context, and the exact question to be answered. “Is this real?” is usually too broad; a better question might be whether a particular region was altered after a stated date, whether two files share a source, or whether the available data can support a specific identification.
Ask the examiner to state the method, validation basis, limitations, uncertainty, and what the analysis cannot establish. DOJ’s December 3, 2024 report on artificial intelligence and criminal justice says, “The output of an AI system should not be the sole basis for conclusions in forensic analysis.” Human interpretation is therefore part of the method, not an optional sign-off after the machine has decided.
Three situations that show the difference between a lead and proof
A video that appears to show an official making an urgent demand
Do not forward the video as confirmed. Preserve the message and URL, verify the agency through an independently found channel, and check whether the request appears on the agency’s official site. If the message asks for money or credentials, treat it as a possible impersonation attempt while the allegation is assessed.
An AI summary names a person in a crowded image
The summary is an interpretation, not an identification. Preserve the original image, determine whether the system was validated for that camera angle and image quality, and seek independent evidence of the person’s identity and presence. Do not publish the name or confront the individual on the basis of the label.
A detector flags a compressed clip as manipulated
Record the detector version, input file, processing history, and result. Determine whether the clip was re-encoded by a platform or captured from a screen, then obtain a better source if possible. A flag can justify forensic review; it cannot by itself establish who altered the file, when the alteration occurred, or whether the depicted event was criminal.
A compact recordkeeping template
For each tip, keep a dated entry containing:
- The exact allegation and the source’s identity or account, if known.
- The original file, message, post, URL, application, username, and date/time information available to you.
- A separate transcription of what the media visibly or audibly contains.
- Every AI-generated description, score, or recommendation, labeled as such.
- Independent checks performed, sources contacted, and results obtained.
- Potential alternative explanations and unresolved gaps.
- The tool name and version, input conditions, validation information, and human reviewer.
- The official channel used for escalation and the reason for the decision.
This record makes uncertainty visible and prevents a fluent machine narrative from becoming the unexamined version of events.
Scope and jurisdiction
The FBI, NIST, and DOJ materials cited here describe U.S. federal practice and recommendations. They do not automatically bind every police department, prosecutor, court, employer, platform, or forensic laboratory. Privacy, recording, disclosure, evidence-preservation, and reporting rules vary by jurisdiction. Follow the policy and law that govern the specific investigation, and obtain legal advice where the consequences are substantial.
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