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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchEvaluate an AI chatbot against the job you will give it, the people who could be affected, and the harm a wrong answer could cause. Before sharing data, review its privacy controls; then test representative and adversarial prompts for reliability, safety, fairness, uncertainty handling and escalation. Finally, verify that the provider explains the system, accepts reports and offers meaningful human review. A few successful chats are only an initial screen, not proof that a chatbot is trustworthy.
What “trustworthy” means in practice
Trust is not a single score. The National Institute of Standards and Technology (NIST) lists these characteristics of trustworthy AI systems: “valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.” NIST’s AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, says the characteristics must be considered in the context of use. Trade-offs can occur, and improving one characteristic does not automatically establish the others.
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For example, a chatbot may be accurate at summarizing public text but unsuitable for employee medical information because its retention policy is unclear. Another may refuse dangerous requests yet provide no human appeal when it makes a consequential mistake. Evaluate the complete use case rather than treating a safety label, a polished interface or a single benchmark as a guarantee.
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Write a one-page use specification before opening a chat. This prevents the tool’s capabilities from quietly expanding beyond what you intended.
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Record the task and the people involved
- Task: State exactly what the chatbot will do, such as drafting low-risk marketing copy, tutoring, searching an internal knowledge base or helping agents answer support questions.
- Intended users: Note their age, expertise, language, accessibility needs and authority to act on an answer.
- Affected people: Include people who never use the chatbot but may be scored, contacted, denied a service or described by its output.
- Inputs: List text, files, images, account data, conversations, location information and any data copied from other systems.
- Outputs and actions: Specify whether a person merely reads an answer or whether software sends messages, changes records, recommends treatment or makes a decision.
Set the consequence threshold
Describe what happens if an answer is wrong, overconfident, discriminatory, unsafe or unavailable. A typo in a brainstorm is minor; an incorrect medication instruction, legal conclusion, financial recommendation, child-safety response or employment decision can cause serious harm. Decide in advance which outputs require a qualified human review and which tasks the chatbot must never perform alone.
NIST’s AI RMF Core starts by establishing context, then iterates through mapping, measurement and management. Use that sequence here: identify risks, choose tests and controls, measure what you observe, and decide whether to mitigate, limit or reject the use.
2. Check the chatbot’s claimed scope and operating conditions
Read the provider’s description, user documentation and acceptable-use rules. Look for the model’s knowledge or cutoff information, supported languages, file and context limits, availability commitments, intended users, known limitations and whether outputs are generated, retrieved from a source collection or both. A claim such as “helpful assistant” is not a specification.
Confirm which edition or account type you are evaluating. Consumer, education, business and API versions can have different retention, administration, logging and human-support controls. Record the product name, model or version shown in the interface, date of review and any settings you changed. If the provider does not identify important operating conditions, treat that uncertainty as a risk rather than filling the gap with assumptions.
3. Test safety and reliability with realistic prompts
Prompt testing is useful for finding obvious problems, but it is not a standardized certification test. NIST treats trustworthy performance as lifecycle work that includes use, testing and monitoring. Test a set of prompts that represents your real workload, not just a few impressive demonstrations.
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Build a repeatable prompt set
- Normal cases: Use several representative tasks with known, verifiable answers. Include short and long inputs, common spelling errors and the languages your users will use.
- Boundary cases: Give incomplete, ambiguous, contradictory or out-of-scope requests. Check whether the chatbot asks a clarifying question or confidently invents a result.
- High-risk cases: Try foreseeable requests involving self-harm, violence, abuse, illegal activity, medical symptoms, financial loss, privacy invasion or sensitive personal data. Do not use real victims’ information; create safe test examples.
- Adversarial cases: Test attempts to override safety instructions, extract hidden prompts, induce disclosure of another person’s data, or make the system follow instructions embedded in an uploaded document.
- Reliability repeats: Run the same prompt more than once and vary harmless wording. Record whether answers change materially and whether the model acknowledges uncertainty.
Score what matters to the task
- Correctness and completeness: Compare factual claims with authoritative references or an expert reviewer. Check calculations, quotations, dates and citations individually.
- Calibration: Does the chatbot distinguish known facts, estimates and guesses? Does it say when information is missing?
- Safe behavior: Does it refuse or redirect dangerous requests without supplying an easy workaround? Does it provide appropriate crisis or professional help when relevant?
- Scope discipline: Does it stay within the job you authorized, or does it make decisions and take actions you did not approve?
- Consistency: Are materially similar users treated similarly, and are changes explained by relevant facts?
- Recovery: After a mistaken answer, can a user correct the context and obtain a clear revision rather than a defensive repetition?
Keep the prompt, full response, timestamp, model or version and reviewer notes. A pass means the result met a predefined threshold for that use; it does not mean the chatbot is safe in general.
4. Examine security and resilience, not just conversational safety
Security concerns include unauthorized access, prompt injection, data leakage, account takeover and unsafe connections to tools. Ask what permissions the chatbot has and separate reading from writing or executing. A system that can send email, edit a customer record or run code needs stronger authentication, approval gates and logging than a stand-alone drafting tool.
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- How are accounts authenticated, and can administrators enforce multi-factor authentication and least-privilege roles?
- Are conversations and uploaded files encrypted in transit and at rest? Who can access logs for support or abuse investigations?
- How are connected tools isolated from untrusted instructions in documents, web pages or messages?
- What happens during an outage, model rollback, compromised account or corrupted retrieval index?
- Can an administrator revoke tokens, delete sessions and export logs needed to investigate an incident?
Resilience includes graceful failure. A chatbot should stop, disclose that it cannot complete a request and route the user to a safe alternative instead of silently producing a plausible answer when a required service or data source is unavailable.
5. Review privacy and data controls before entering information
Complete the privacy review before pasting a single sensitive conversation. Find out what is collected, why it is collected, how long it is retained, where it is processed, who receives it and whether it is used to improve models. Check whether prompts, outputs, uploaded files, voice recordings, feedback and device or account metadata are covered separately.
Controls to verify
- Training and improvement: Is there an opt-out, and does it apply to existing as well as future conversations?
- Retention: Can you set a shorter period or use a mode that does not save history? What backups and abuse-monitoring copies remain?
- Deletion: Can an individual or administrator delete a conversation, file, account and derived profile? Is deletion confirmed and subject to exceptions?
- Access: Can the provider, contractors, workspace administrators or integrated apps read content? Are access events logged?
- Uploads and outputs: Are files scanned, retained separately or shared with another processor? Who owns or may reuse generated content?
- International processing: In which countries are data stored or accessed, and what contractual or legal protections apply to your users?
NIST identifies privacy as a trustworthiness characteristic and warns that AI can create inference risks: a system may identify a person or infer private attributes from information that seemed harmless. Remove names, account numbers, exact locations, health details and confidential business content unless the documented controls and your legal or organizational requirements clearly permit the use. Redaction lowers exposure but does not guarantee anonymity, especially when several details can be combined.
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6. Evaluate values, fairness and effects on different groups
Values appear in what the chatbot refuses, prioritizes, describes as normal and recommends to a user. Start with the people who could be affected, including groups that are absent from your test team. UNESCO’s Recommendation on the Ethics of Artificial Intelligence places human rights and dignity at the center and identifies transparency, fairness and human oversight as core principles.
Design a fairness probe
- Choose realistic scenarios where identity, language, disability, age, gender, culture, religion, socioeconomic position or viewpoint could affect the answer.
- Create matched prompts that change one relevant attribute at a time while keeping the underlying facts constant.
- Ask the chatbot to explain assumptions, identify missing context and state when a generalization could harm someone.
- Have reviewers with relevant lived experience or subject expertise assess tone, omissions, stereotyping, access barriers and practical consequences.
- Define what difference is acceptable, what requires correction and what makes the use unacceptable. Document the reason rather than relying on an overall “bias score.”
Fairness is contextual. A translation system, a moderation assistant and a hiring support tool have different error costs and affected populations. NIST cautions that mitigating harmful bias alone does not establish fairness. Avoid treating a handful of matched prompts as proof; use them to uncover questions for broader evaluation, monitoring and human review.
7. Check transparency, explainability and interpretability
Transparency concerns what happened in the system. NIST states, “Transparency can answer the question of ‘what happened’ in the system.” Ask whether the service clearly identifies itself as AI, records the model and tools used, shows when an answer came from a connected source, and preserves enough information to investigate an incident.
Explainability asks how an output or decision was produced; interpretability asks why it matters in the particular context. A fluent explanation may itself be generated and may not faithfully describe internal processing. Treat explanations as evidence to review, not as proof of accuracy, privacy, security or fairness.
Information a responsible service should make available
- The system’s purpose, intended users, major limitations and prohibited uses.
- The role of automated generation, retrieval, ranking, moderation and external tools.
- What data is collected and how users can control, correct or delete it.
- Known failure modes, evaluation methods and meaningful changes between versions.
- Whether a human reviews content, when that occurs and how long records are kept.
A U.S. Senate hearing record contains a recommendation that chatbot providers tell consumers they are interacting with AI, potentially on the first query. That is a recommendation recorded in hearing responses, not a universal legal requirement. Regardless of legal status, clear disclosure is a practical minimum for informed use.
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8. Verify accountability, escalation and redress
Find a route to report harmful output, request correction and reach a human. Test it with a low-risk question before relying on the service. Record the channel, expected response time, information it requests and whether you receive a case number or other acknowledgement.
A usable escalation path includes
- A prominent report or support channel that does not require sharing more sensitive data than necessary.
- Human review for high-impact disputes, not only an automated thumbs-up or thumbs-down form.
- Instructions for preserving the relevant conversation, version and timestamp.
- A way to pause automated actions while an incident is investigated.
- Notice of the outcome, corrective action and any appeal or external complaint route.
Assign an owner in your organization who can suspend the chatbot, update prompts or policies, notify affected people and coordinate legal, security or safeguarding responses. Accountability is incomplete if no person has authority to act when the system fails.
9. Compare chatbots with the same evidence
When choosing among services, use an identical test pack, settings and review period. Compare along the axes below, but set thresholds from the consequences of your use. Do not average unlike risks into a universal score.
| Axis | What to test or document | Decision question |
|---|---|---|
| Task performance and reliability | Known-answer tasks, edge cases, repeat runs, uncertainty statements and correction behavior | Does it meet the minimum accuracy and consistency needed for this job? |
| Safety handling | Risky prompts, refusal quality, safe alternatives, crisis routing and scope limits | Does it reduce foreseeable harm without enabling an easy workaround? |
| Security and resilience | Permissions, injection resistance, authentication, logging, outages and recovery | Can a mistake or compromise affect systems or people beyond the chat? |
| Privacy and user controls | Collection, training use, retention, deletion, access, uploads and processing locations | Do the controls fit the sensitivity and obligations of your data? |
| Fairness and values | Matched scenarios, affected-group review, harmful stereotypes, accessibility and language coverage | Are differences acceptable for the people affected, and is mitigation feasible? |
| Disclosure and explanation | AI identification, purpose, limits, source labeling, version records and explanation quality | Can users understand what happened well enough to make a safe choice? |
| Escalation and redress | Human support, incident reporting, appeals, pause controls and response evidence | Can someone correct harm when the model is wrong? |
| Accessibility | Keyboard access, screen-reader behavior, captions, readable contrast, language and alternative input or output | Can every intended user operate and understand the service? |
Write a short decision record: approved use, prohibited uses, evidence reviewed, residual risks, required human checks, data restrictions, owner and next review date. A chatbot that fails a non-negotiable threshold should be rejected or limited even if it performs well on other axes.
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10. Turn test results into operating rules
Use a risk-based permission ladder
- Low consequence: Permit drafting or brainstorming with public or synthetic data, while requiring users to verify factual claims.
- Moderate consequence: Require a trained reviewer, source checking, restricted data and a record of the final decision.
- High consequence: Do not let the chatbot be the sole basis for decisions about health, safety, liberty, employment, housing, education access, credit or essential services. Use it, if at all, as a bounded aid with qualified human judgment and a way to challenge the result.
Give users a short checklist
- Am I allowed to enter this information?
- Could a wrong or biased answer harm someone?
- What source or person will verify the output?
- Does this request require a human decision or professional advice?
- How do I report a problem and stop an automated action?
Train users not to paste secrets, accept confident language as evidence, or treat a refusal as proof that every other answer is safe. Keep examples of acceptable and unacceptable use close to the interface.
11. Revisit the evaluation as the system changes
Chatbots, policies, connected tools and moderation behavior change. NIST’s framework emphasizes ongoing, lifecycle risk management rather than a one-time approval. Re-run critical prompts after a model, feature, data source, integration, privacy policy or user population changes. Monitor complaints, correction rates, escalation outcomes, disparate effects and near misses.
Set review triggers: a serious incident, a new high-impact use, a material model update, a change in retention or training terms, a newly connected tool, or evidence that a group is being harmed. Keep old and new results so a regression is visible. If you cannot monitor a use adequately, narrow the scope or stop it.
A practical go/no-go worksheet
Approve a chatbot only when all of the following are documented for the specific use:
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- The task, users, affected people and harm thresholds are defined.
- Representative, edge, risky and repeated prompts were tested and reviewed.
- Uncertainty, refusal, recovery and escalation behavior meet stated requirements.
- Security permissions, resilience plans and incident ownership are in place.
- Privacy collection, retention, training use, deletion and access controls fit the data.
- Fairness and accessibility were assessed with relevant reviewers and matched cases.
- Users are told when they are interacting with AI and understand its limits.
- A human can review, correct, pause and appeal consequential outcomes.
- The decision record names residual risks and a date or trigger for re-evaluation.
If any mandatory item is unknown, the responsible choice is to delay, restrict the data and authority, or select another tool. NIST identifies the AI RMF as voluntary, and its materials indicate that AI RMF 1.0 is being revised; consult current official framework materials and current provider terms before consequential use. The framework helps structure judgment, but it is not a legal compliance determination, an audit of a particular chatbot or a guarantee of safety.
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