The Tool Desk
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1. Stop and assess the risk
Do not follow instructions that could put you or someone else at risk. Pause the interaction if continuing could increase distress or expose more personal information. Consider whether the output points to an immediate threat, self-harm, violence, exploitation, or another urgent safeguarding concern. What to do next depends on the person’s location and the nature of the danger.
2. Get human help if someone may be in danger
If there is imminent physical danger, contact the relevant local emergency service. For emotional crisis support in the United States, the 988 Suicide & Crisis Lifeline is an option; it is not a universal emergency number or a worldwide crisis service. Elsewhere, use an appropriate local resource.
If the situation involves a school, workplace, or other institution, contact a trusted adult, supervisor, safeguarding lead, or other responsible person as appropriate. UK Department for Education standards for learner-facing generative AI products say that users experiencing distress should be directed to human help, such as teachers, family, peers, or crisis services. That is education-specific guidance, not a rule for every AI service.
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3. Keep a concise, privacy-conscious record
Where it is safe and permitted, note the service or tool, the date and time, the relevant prompt and response, and enough context to explain what made the output unsafe. A screenshot or export may help if the service allows it. Keep unrelated sensitive information out of the record, and avoid reposting harmful material publicly.
The UK Department for Education’s standards for covered learner-facing products discuss robust activity logging, including recording prompts and responses. Those product expectations do not establish a universal record-keeping duty for every AI user or consumer service.
4. Report the output and escalate where appropriate
Use the service’s current in-product report, feedback, or safety channel. Explain plainly what was unsafe and provide only the relevant context. Reporting interfaces differ, so consult the service you used rather than assuming one provider’s steps apply to another.
If the tool was supplied by a school, employer, or other organization, notify its appropriate safety, safeguarding, or IT contact. In the UK education context, Department for Education standards call for a formal complaints mechanism and relevant supervisor or safeguarding alerts. These standards are specific to that setting and should not be mistaken for global law.
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Check medical, legal, financial, safety, and identity-related claims with qualified people or authoritative sources before relying on them. NIST’s voluntary AI Risk Management Framework identifies validity, reliability, safety, security, accountability, privacy, and fairness as characteristics of trustworthy AI, while noting that addressing them individually does not guarantee overall trustworthiness. The framework helps organizations manage risk; it does not certify that a particular tool is safe.
The Federal Trade Commission has also warned about inaccuracy, bias, classification errors, and failures to account for context in AI tools used to address online harms. That warning supports caution, but it is not an error rate for all chatbots or generative AI systems. In its June 2022 release about its report to Congress, FTC Bureau of Consumer Protection Director Samuel Levine said, “Our report emphasizes that nobody should treat AI as the solution to the spread of harmful online content.” His statement concerned using AI to address harmful online content, not every chatbot output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to expect from safety controls
AI safety controls can fail, and no single reporting process applies across vendors or jurisdictions. NIST’s framework is voluntary, while the UK Department for Education’s standards address a specific educational context. Neither establishes that every AI service has the same safeguards, reporting route, or human review process.
No comparable published figure in the cited sources establishes how often user-facing AI tools produce harmful or unsafe output. The FTC’s 2022 report is a qualitative warning about certain AI uses, not a current prevalence estimate.
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