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Evaluate an AI chatbot before trusting it by matching its claimed capabilities to your specific use, testing realistic and risky situations, reading its data practices, examining effects on affected groups, and confirming that a person can review or correct harmful outputs. Do not treat a polished conversation or a handful of successful prompts as proof that a system is safe, fair, or reliable.
A practical evaluation produces a documented decision: what the chatbot may do, what information it may receive, when it must defer to a human, and when you will reassess the decision. The National Institute of Standards and Technology (NIST) describes trustworthiness as contextual and multifaceted, so there is no universal pass score that applies to every chatbot or every use.
What “safe and values-aligned” means
“Safe” does not mean merely that a chatbot refuses a few obviously dangerous requests. It means the system’s behavior is acceptable for the people, information, and consequences involved in your use. A chatbot that is adequate for brainstorming travel itineraries may be unsuitable for medical triage, employment decisions, financial guidance, education discipline, or any task involving confidential records.
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“Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.”
These characteristics interact. Improving one does not automatically establish the others, and trade-offs can occur. A system can be transparent about its limitations yet still produce inaccurate answers; it can be accurate on average while exposing private information; it can refuse dangerous content while treating a particular group unfairly. NIST says the characteristics must be considered in the context of use, through ongoing risk management rather than a one-time demonstration.
AI RMF 1.0 was released on January 26, 2023. NIST’s Generative AI Profile, NIST-AI-600-1, was released on July 26, 2024. NIST describes the framework as voluntary and indicates that AI RMF 1.0 is being revised. Use the current official material and the provider’s current policies when making a consequential decision; this guide is a method, not an audit or legal-compliance determination.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsStep 1: Define the use, people and stakes
Start before opening a chatbot. Write down what you want it to do and what could happen if it is wrong, discriminatory, manipulated, unavailable, or overly confident. NIST’s AI RMF Core begins by establishing context and then iterating through mapping, measurement, and management.
Describe the task precisely
Replace a broad goal such as “help our team make decisions” with a bounded task such as “draft a first-pass summary of customer emails for a trained employee to review.” State whether the chatbot is generating text, retrieving information, classifying cases, recommending an action, or taking an action automatically. The more authority the system has, the stronger your controls must be.
List intended users and affected people
Identify who will operate the chatbot and who may be affected without using it. Include customers, employees, applicants, students, patients, bystanders, and people whose data appears in uploaded documents. Consider language, disability, age, cultural context, and access to a human alternative. A system can appear helpful to its direct user while imposing errors or extra work on someone else.
Inventory information and consequences
Record every category of information the chatbot will receive or infer. This may include names, contact details, account data, health information, employment records, location, financial details, confidential business material, or conversational history. Then describe plausible harms from a wrong or biased response: physical injury, loss of money, denial of an opportunity, privacy exposure, reputational damage, or inability to obtain help.
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Set a human-deference threshold
Decide in advance when the chatbot must stop, ask for a person, or provide only general information. Define who reviews the output, how quickly they respond, and what records they need. For a high-consequence use, the person should have genuine authority to override the system rather than merely approving its recommendation.
| Planning question | Lower-stakes example | Higher-stakes control |
|---|---|---|
| What is the chatbot allowed to do? | Suggest alternative wording for an internal announcement. | Draft only; a named professional approves every message before it reaches a customer or patient. |
| Who can be affected? | The employee requesting brainstorming help. | Applicants, employees, or customers who may receive a classification or recommendation. |
| What happens after an error? | A user edits an awkward sentence. | The case is paused, a human investigates, the affected person can challenge the result, and the incident is recorded. |
| What information is necessary? | Public facts and non-sensitive notes. | Only the minimum fields needed, with sensitive identifiers removed or masked. |
Step 2: Check safety, reliability and boundaries in context
Run a small, repeatable test set that reflects the real task. The purpose is to find concerns and define safeguards, not to certify the chatbot. NIST treats evaluation as lifecycle work that includes intended use, testing, monitoring, and management.
Build representative prompts
Use the same prompts for every candidate system and save the date, model or product version shown to you, settings, and outputs. Include ordinary requests, ambiguous cases, foreseeable misuse, and situations in which the correct response is uncertainty or referral.
| Prompt category | What to test | Signals to look for |
|---|---|---|
| Normal task | A typical request using realistic but non-sensitive data. | Accuracy, completeness, consistency, and whether the result stays within the stated scope. |
| Ambiguity | A request missing a fact that materially changes the answer. | Asks a clarifying question instead of silently guessing. |
| Uncertainty | A question for which the system may not have enough evidence. | Clearly states limits, distinguishes facts from inferences, and suggests how to verify. |
| Risky request | A foreseeable request that could cause physical, financial, privacy, or legal harm. | Refuses or safely redirects, explains the boundary, and offers an appropriate alternative. |
| Scope pressure | Ask it to perform a task outside its advertised role. | Does not claim expertise or authority it lacks. |
| Adversarial wording | Contradictory instructions, prompt injection, or an attempt to override safeguards. | Maintains the task boundary and does not reveal protected instructions or confidential context. |
| Consistency | Repeat equivalent questions with changed wording and order. | Materially similar answers, or a transparent explanation for differences. |
| Escalation | Tell it the situation is urgent, disputed, or beyond its competence. | A clear route to a qualified person rather than false reassurance. |
Assess not only whether an answer sounds plausible but also whether the chatbot communicates uncertainty in a way a normal user can understand. Check for invented sources, fabricated quotations, omitted assumptions, and recommendations that exceed the information provided. If the service gives confidence scores or citations, verify what they actually represent; a label or number is not evidence of correctness by itself.
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Observe what happens when a user edits, exports, shares, or deletes a conversation. Check whether a failed request can be retried safely, whether an outage leaves people without an alternative, and whether the system logs enough information to investigate an incident without retaining unnecessary personal data. Security and resilience include protection against unauthorized access and the ability to recover from failures, not only the wording of an individual response.
A few prompts can reveal obvious problems, but they cannot establish overall safety, fairness, or reliability. Treat results as an initial screen that informs a pilot with monitoring and human review.
Step 3: Read privacy and data practices before sharing information
Review the service’s privacy notice, terms, settings, and administrator controls before entering real data. Do not assume that a chat is private because it is not publicly visible. AI systems can create privacy risks by enabling identification or inference of information that a person did not explicitly provide.
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Questions the provider should answer
- What conversation text, files, images, voice recordings, device data, and account information are collected?
- How long are prompts, outputs, logs, and backups retained, and where are they processed?
- Are conversations or uploads used to improve models, evaluate quality, or train human reviewers? Is there a meaningful opt-out?
- Who can access the data, including contractors, administrators, or other customers in a shared environment?
- What controls let a user view, export, correct, or delete information, and how long do deletion requests take to propagate?
- What happens to uploaded content after a chat, account closure, subscription change, or security incident?
- What safeguards apply to children’s data, sensitive categories, and cross-border transfers?
Look for specific answers rather than assurances such as “we take privacy seriously.” Distinguish an individual account from an organization-managed plan: retention, training use, access logs, and administrator visibility may differ. If a term is unclear, ask the provider or your privacy officer before proceeding.
Minimize what you disclose
Use synthetic or redacted examples during evaluation. Remove names, account numbers, exact dates of birth, credentials, private keys, and other identifiers unless they are essential and the approved controls cover them. Remember that combinations of harmless-looking details can identify a person, and a model may infer sensitive attributes from ordinary text.
If the terms and controls do not fit your risk tolerance, do not upload the information. A convenient interface is not a reason to accept an unknown retention or reuse practice.
Step 4: Examine values, fairness and human impact
Values are visible in what a system treats as normal, whose interests it prioritizes, how it handles disagreement, and who bears the cost of an error. Start by identifying the relevant rights and interests in your use. UNESCO’s Recommendation on the Ethics of Artificial Intelligence foregrounds human rights and dignity and identifies transparency, fairness, and human oversight as core principles.
Test meaningful differences
Construct paired prompts that should receive equivalent treatment when you change one relevant attribute, such as name, language, disability-related wording, cultural reference, or viewpoint. Also test cases where the difference should matter—for example, a request for an accommodation or a safety warning. Record not only the final answer but tone, refusal rate, assumptions, level of detail, and whether the system asks for unnecessary personal information.
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Check the provider’s stated values and limits
Look for documentation of intended uses, excluded uses, known limitations, evaluation methods, feedback channels, and human-oversight arrangements. Ask whose perspectives informed those choices and how people can report harm. Vague claims about being “neutral” are less useful than a clear account of where the system may fail and what a user should do.
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Include accessibility and language access
Have intended users try the complete workflow with the assistive technologies, languages, reading levels, and devices they actually use. Check whether safety notices, refusals, citations, and escalation options are understandable and available in those modes. An inaccessible safeguard is not an effective safeguard for the person who cannot use it.
Step 5: Verify transparency, explainability, accountability and recourse
Transparency, explainability, and interpretability are related but distinct. NIST summarizes transparency as answering “what happened” in the system. Explainability concerns how a result was produced; interpretability concerns why that result has meaning in its context. None of these qualities by itself guarantees accuracy, privacy, security, or fairness.
Confirm that people know they are interacting with AI
The service should make its AI nature clear at the point of interaction, not hide it in a terms page. A response recorded in a U.S. Senate hearing recommended informing consumers that they are interacting with AI, potentially on the first query. That is a recommendation in hearing responses, not a universal legal requirement. Clear disclosure is especially important when a user might mistake generated text for a professional, government, or human representative.
Ask what can be explained
Find out whether the system can show the source material, relevant input, version, or rule behind an answer. Be cautious when an explanation is merely a plausible story generated after the fact. For a high-impact use, require a review process that can examine the underlying record and not just the chatbot’s narrative.
Demand a usable path to correction
Identify how to report an unsafe output, request human review, correct inaccurate information, appeal a decision, and obtain a response. Test the route with a harmless issue before deployment: can you find it, does it create a case number, who owns the case, and is there a stated response target? Keep an internal incident log even when the provider’s process is external.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 6: Compare chatbots using the same protocol
When several systems could meet the need, compare them on identical, task-relevant prompts and controls. Do not combine unrelated features into a universal score. NIST says the importance and trade-offs of trustworthiness characteristics depend on context.
| Comparison axis | Evidence to collect | Decision question |
|---|---|---|
| Task performance and reliability | Accuracy checks, consistency runs, uncertainty handling, and failure examples. | Does it meet the minimum quality needed for this task, with a human able to verify it? |
| Safety handling | Responses to risky, ambiguous, adversarial, and out-of-scope prompts. | Does it prevent foreseeable harm and escalate appropriately? |
| Privacy and user controls | Retention, training use, deletion, access, upload handling, and administrative settings. | Can the data practices be made consistent with your obligations and risk tolerance? |
| Fairness and values | Paired tests, group-specific error review, accessibility checks, and provider documentation. | Are affected people treated acceptably, with a way to detect and correct harm? |
| Disclosure and explanations | AI disclosure, limitations, sources, version information, and explanation quality. | Can users understand what happened and make an informed judgment? |
| Escalation and redress | Human handoff, incident reporting, appeals, response ownership, and records. | Can a person intervene before or after a harmful result? |
| Accessibility | Keyboard and screen-reader use, language support, readability, and device performance. | Can every intended user reach the same safeguards and alternatives? |
Set pass/fail thresholds before looking at results, and weight the axes according to the consequences of your use. For example, privacy and human review may be gating requirements even if another system writes more fluent text. Keep raw prompts and outputs so a later reviewer can reproduce the comparison.
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Step 7: Make a use-specific decision and document it
Choose one of three practical outcomes, then record the reasoning.
| Outcome | When it fits | Required controls |
|---|---|---|
| Use with guardrails | The system meets minimum thresholds and remaining risks are understood and manageable. | Approved tasks, prohibited inputs, human review, monitoring, incident handling, and a named owner. |
| Limited pilot | Potential value exists, but evidence or controls are incomplete. | Small scope, synthetic or redacted data, extra review, a stop condition, and a scheduled reassessment. |
| Do not use | A critical threshold fails, the provider will not answer material questions, or no effective human alternative exists. | Choose another process and document why the chatbot was rejected. |
Save the use case, affected groups, data categories, test prompts, outputs, provider terms, product version, date, decision owner, unresolved concerns, and review date. Tell users what the chatbot may and may not do. Revisit the decision after a major model or policy change, a serious incident, a new population or language, or a change in the consequences of the task. A chatbot’s behavior and controls can change even when its interface looks the same.
A printable evaluation checklist
- Purpose: The task, intended users, affected people, and prohibited uses are written down.
- Stakes: Likely harms and a clear human-deference threshold are defined.
- Testing: Normal, ambiguous, uncertain, risky, adversarial, accessibility, and escalation prompts have been run.
- Reliability: Outputs are checked against authoritative information, and important uncertainty is visible.
- Privacy: Collection, retention, training use, access, deletion, and upload handling are understood.
- Minimization: Evaluation uses synthetic or redacted data unless approved controls justify real information.
- Fairness: Relevant groups and viewpoints have been considered, with paired or group-specific tests where appropriate.
- Values: Human rights, dignity, oversight, and the interests of people who do not operate the chatbot are addressed.
- Transparency: Users are told they are interacting with AI and are given understandable limitations.
- Accountability: A person can report harm, obtain review, correct information, and appeal an outcome.
- Accessibility: Intended users can use the interface and reach the same warnings and human alternatives.
- Governance: An owner, incident log, stop condition, and reassessment date are assigned.
Common evaluation mistakes and how to recover
Mistaking fluent writing for reliable knowledge
Problem: The chatbot sounds confident, so users stop checking it. Recovery: Require source verification or independent review for claims that affect a decision, and test how it responds when evidence is missing.
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Testing only friendly, ordinary prompts
Problem: The system passes a demonstration but fails under ambiguity, pressure, or misuse. Recovery: Add edge cases, contradictory instructions, privacy-sensitive requests, and explicit escalation tests to the shared test set.
Reading privacy terms after uploading real records
Problem: Sensitive information is already in logs or backups before anyone understands retention. Recovery: Stop the transfer, follow the provider’s deletion and incident procedures, notify the responsible privacy or security lead, and use redacted data for further testing.
Equating disclosure with trustworthiness
Problem: A banner saying “AI-generated” is treated as evidence that outputs are safe. Recovery: Evaluate disclosure alongside accuracy, privacy, fairness, security, human review, and recourse.
Using the chatbot as the sole basis for a high-consequence decision
Problem: A person cannot challenge an error, and responsibility becomes unclear. Recovery: Restrict the system to assistive drafting or information gathering, require qualified human judgment, and provide an accessible appeal route.
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Ignoring people who are not the direct user
Problem: Convenience for staff shifts errors or surveillance burdens onto customers, applicants, or employees. Recovery: Include affected representatives in testing, publish meaningful limits, and measure whether they can obtain human help.
Limits of this method
This checklist helps you make a use-specific decision; it does not certify a chatbot, establish legal compliance, or guarantee future behavior. Prompt testing is necessarily limited, and fairness or security may require specialist audits, representative data, and ongoing monitoring. NIST’s AI RMF is voluntary, and its materials are being revised, so confirm the current framework and the provider’s current terms before a consequential deployment.
Bottom line
Use an AI chatbot only after you can explain its permitted role, the information it will receive, the harms you are guarding against, and the exact point at which a human takes over. Compare candidates with the same realistic tests, reject systems whose privacy or accountability answers are inadequate, and schedule a review because trustworthiness is a continuing responsibility rather than a one-time impression.
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