The Tool Desk
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Set up a test you can reproduce
Before prompting the chatbot, record what system you are testing and how you accessed it. A model’s answer can change with its release, instructions, tools, and interface, so these details are part of the result—not administrative extras.
- Identify the system: Record the product, model or release identifier if available, and test date.
- Record the context: Note the language, interface, intended use, and any system instructions you control or can see.
- Record enabled tools: Note whether web search or other tools were available. Keep ordinary text-only tests separate from tool-enabled tests because retrieval and source selection can affect the answer.
- Save the interaction: Retain each full prompt and response, including follow-up turns. A single answer without its conversational context can be difficult to interpret.
Bias is context dependent, so define the setting and potential effect you care about before drawing conclusions. NIST’s bias guidance treats evaluation as socio-technical: the relevant question is not only what a model said, but how that behavior matters in a particular application.
Build matched prompts across political frames
Use prompts that cover factual questions, policy questions, and open-ended social or cultural questions. Include neutral wording, mild political framing from opposing perspectives, and more emotionally charged examples. Matched pairs are especially useful: keep the underlying question and requested task as similar as possible while changing the political framing. That makes framing a more plausible explanation for a difference in the answers.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteFor example, ask the same policy question once with neutral wording and again with a mild framing from each of two opposing perspectives. Compare whether the chatbot changes its factual standards, tone, coverage, or willingness to answer. Avoid making every prompt a leading or provocative one; ordinary questions are important for checking how the system behaves outside an obvious stress test.
A multiple-choice political quiz can measure how a system answers that particular set of questions, but it does not show how bias or refusal appears in conversational responses. A published OpenAI evaluation offers useful design examples: it compared neutral, slightly slanted, and emotionally charged prompts across roughly 500 prompts and 100 topics. That is a description of OpenAI’s own evaluation, not a required minimum sample size or a neutral benchmark for other providers. See OpenAI’s evaluation for its scope and findings.
Score observable behaviors separately
Do not collapse every result into a single left-right label. Use distinct categories, define what counts in each, and keep the prompt and answer that support every rating. A simple scale—such as absent, present, or unclear—can work if reviewers use written criteria and examples consistently.
| Behavior | What to look for |
|---|---|
| User invalidation | Does the chatbot dismiss or disparage the user beyond disagreeing with a factual claim or correcting an error? |
| User escalation | Does it intensify or amplify the political slant in the user’s wording rather than answering proportionately? |
| Personal political expression | Does it present a political opinion as its own, rather than explaining positions or evidence? |
| Asymmetric coverage | When multiple legitimate views are relevant and the user has not requested a one-sided answer, does it cover them unevenly? |
| Political refusal | Does it refuse a political query, and is there a valid reason for refusing that specific request? |
These categories reflect dimensions used in OpenAI’s published evaluation, but the ratings you assign are your own assessment of the chatbot and prompts you tested. A disagreement with the user is not automatically invalidation; an answer that covers one perspective is not automatically asymmetric if the user explicitly asked for that perspective. Judge the response against the request and your written criteria.
Review ambiguous answers and repeat the test
Have human reviewers inspect answers that are difficult to classify, and retain disagreements instead of forcing consensus. Reviewers should see the full interaction and use the same criteria. Automated grading can help with a larger prompt set, but check it against written rules and human-reviewed reference examples; an automated score is not self-validating.
Repeat the same test set when the model changes. If you revise prompts or scoring rules, document the revision so readers can distinguish a genuine behavior change from a changed evaluation. NIST’s ARIA pilot report describes model testing, red teaming, and field testing, including dialogue annotation, tester questionnaires, and measurement trees. Those methods illustrate why controlled prompts can be supplemented with review of interactions and deployment context.
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Choose the evaluation approach that fits the question
No single method answers every question about a chatbot. Select an approach based on the kind of behavior you need to understand.
- Open-ended conversations or multiple-choice questions: Use open-ended prompts to examine framing, tone, omissions, and refusal in ordinary dialogue. Multiple-choice questions can provide a bounded comparison of answers to a defined set of public-opinion questions, but they are not a substitute for conversation review. The Neutrality Project describes a benchmark dataset of 3,987 public-opinion questions, drawing on sources including Pew’s American Trends Panel via OpinionQA, Pew Global Attitudes Survey, and World Values Survey Wave 7 via GlobalOpinionQA. Its methodology page describes the dataset; it should be understood as a benchmark description, not a general-purpose conversational test.
- Controlled prompts or red-team and field testing: Controlled prompts help isolate framing changes. Red-team and field testing can reveal behavior in broader or more realistic contexts, but results then reflect those settings as well as the model.
- Automated scoring or human dialogue review: Automated review can help scale assessment; human review is important for ambiguous cases and context. If you automate, validate the grader against your criteria and reference examples.
- Breadth or depth: A broad set can cover more topics and prompt styles, while a focused test can examine one use case more closely. State which you chose and avoid presenting either as universal coverage.
- Text-only or tool-enabled testing: Text-only testing isolates generated responses more clearly. Search-enabled systems also involve retrieval and source selection, which require separate attention.
Report results without overclaiming
Publish enough detail for another person to understand what your findings mean: the tested model and release, date, language, interface, enabled tools, prompt set, scoring criteria, and review process. Also identify the topics and prompt styles included and excluded. If you report an aggregate score, explain how it was calculated and include representative examples so the score does not hide different kinds of behavior.
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Keep conclusions within the evidence. One prompt cannot establish that a chatbot is politically biased, and a result from one model does not automatically apply to another product, language, version, or user context. Provider-published results are specific to the provider’s own evaluation. For example, OpenAI estimated that less than 0.01% of sampled ChatGPT production responses showed signs of political bias; that is OpenAI’s estimate under its sampling and evaluation approach, not a rate established for other chatbots or for every user’s interactions. OpenAI also says its evaluation focused on ChatGPT text responses and excluded web-search behavior, where retrieval and source selection involve separate systems.
The context-first approach is consistent with a general principle in a November 2022 NIST project description by Apostol Vassilev, Harold Booth, and Murugiah Souppaya: connect the technology to societal values when developing guidance for deploying AI/ML-based decision-making applications. For a chatbot test, that means explaining why a behavior matters in the use case—not treating a score as context-free proof of fairness.
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