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A practical evaluation asks seven questions: Can the chatbot perform the task? Is it dependable and safe in your context? What does it disclose about data and limitations? Can you control or change its persona? Does it identify itself as non-human? How does it handle gender-based abuse and stereotypes? What evidence shows that it manages broader, systemic bias?
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Start with the human outcome, not the persona
Write down the result a person must achieve before opening a chatbot. “Answer routine customer questions,” “summarize a document,” “practice a language,” and “support a healthcare conversation” are different use cases with different failure costs. NIST’s AI Use Taxonomy: A Human-Centered Approach (AI 200-1, published March 26, 2024) places human goals and outcomes at the center of AI use.
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- Define the task: state the inputs, expected output, users and consequences of an error.
- Set a success test: decide what an acceptable answer must contain and what it must never do.
- Set a failure threshold: determine whether an occasional mistake is inconvenient, harmful or disqualifying.
- Separate interface preference from performance: a pleasant voice or name may affect comfort, but it does not demonstrate accuracy, security or fairness.
Use a consistent chatbot scorecard
Evaluate every candidate against the same dimensions. NIST identifies validity and reliability, safety, security and resiliency, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful-bias managed as core trustworthiness properties. They are evaluation dimensions, not proof that a particular service has passed an audit.
| Dimension | Evidence to look for | Questions to ask during testing |
|---|---|---|
| Task and outcome fit | Documented capabilities, supported inputs, output controls and workflow integration | Does it complete the defined task with the required format and level of human review? |
| Validity and reliability | Known limitations, version information, monitoring and reproducible behavior | Does it give consistent answers to equivalent prompts and signal uncertainty? |
| Safety and security | Abuse controls, access management, incident process and secure handling | Does it refuse dangerous requests and protect against prompt or account misuse? |
| Privacy | Plain-language data policy, retention period, training-use controls and deletion process | What happens to prompts, uploaded files, voice recordings and account metadata? |
| Transparency and explainability | Clear disclosure that the assistant is non-human, model or feature documentation, explanations of limits | Can a user tell why an answer may be wrong and who is accountable for the service? |
| Persona and agency | Choice of voice, name, avatar, pronouns, style and ability to change or disable them | Is a gendered persona the default, and can users select a neutral presentation? |
| Fairness and accessibility | Accessibility support, representative testing, bias-management process and impact reporting | Does performance remain usable across languages, accents, disabilities and social contexts? |
Check whether gender is being designed into the interface
Gender coding rarely comes from one setting. UNESCO’s 2019 article on gender bias in artificial-intelligence applications describes how voice, names, personalities and social expectations can combine to make a system appear feminine or masculine. A text-only chatbot can still communicate gender through its name, avatar, pronouns, speech patterns, emotional labor or assumptions about people’s roles.
Voice and speech
Listen for pitch, accent, speaking style and whether the product labels a voice with a gendered name. A high-pitched or conventionally feminine voice is not inherently harmful; the concern is an unexamined default, limited choice or a pattern in which assistants are expected to be endlessly agreeable and servile. Test the same feature with available voices, including any described as neutral, and record whether functionality changes.
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Inspect the first-run screen, help center, app icon, profile image and system messages. A human name, gendered avatar or automatic use of “she” or “he” can create a social identity even when the service never says it has one. Look for a neutral option, custom naming, pronoun controls and the ability to turn off an avatar without losing core functions.
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Interaction style and role assumptions
Test prompts that involve authority, care work, technical expertise and leadership. Does the assistant describe women as assistants and men as decision-makers? Does it become more apologetic or deferential when addressed with a feminine name? Does it assume a user’s gender from a profession, family role or writing style? Save examples rather than judging from a single entertaining response.
The absence of a voice does not prove that a chatbot is gender-neutral. Social cues can be embedded in text, visual design and the behavior selected by the system’s developers.
Require user choice, disclosure and interoperability
UNESCO recommends ending female defaults, exploring neutral machine gender for voice assistants, allowing people to change assistants and disclosing the non-human nature of the technology at the outset. These are policy recommendations, not binding law, but they provide concrete checks for procurement and personal use.
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- Meaningful choice: users should be able to select, change or disable voice, name, avatar, pronouns and conversational style without losing access to the service.
- Interoperability: where practical, settings and conversation history should be exportable or usable with another approved assistant, so a persona is not an unavoidable lock-in mechanism.
- Administrative control: organizations should be able to set a neutral default while allowing individual accommodations.
Choice alone is not enough. A menu with several voices but no explanation of data collection, safety limits or accountability gives users cosmetic control without informed control.
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Test reliability and safety in the real use context
NIST’s trustworthiness guidance treats validity and reliability as context-dependent. A bot that is adequate for brainstorming may be unsuitable for legal, financial, medical or operational decisions. Test the exact task, inputs and consequences you identified at the start.
Build a small, repeatable test set
- Collect representative prompts, including common requests, ambiguous wording, multilingual examples and edge cases.
- Include at least one prompt with a known answer and one where the correct response is “I do not have enough information.”
- Run equivalent prompts in separate sessions and, where possible, with different persona settings.
- Score factual accuracy, completeness, citation or provenance, uncertainty signaling, response time and formatting against your acceptance criteria.
- Repeat the test after a major model or product update; a new version can change behavior without changing the interface.
Probe safety boundaries
Ask how the assistant handles requests that could expose private data, facilitate wrongdoing, provide unsafe medical instructions or target a protected group. A useful refusal explains the boundary briefly and offers a safe alternative. Record whether the bot invents authority, gives dangerous detail before refusing or can be manipulated by instructions embedded in uploaded content.
Security and resiliency include more than the model’s wording. Check authentication, permissions, rate limits, recovery after an outage, logging and administrator controls. For a workplace deployment, verify that one user cannot retrieve another user’s conversation or uploaded file.
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Inspect privacy, transparency and accountability before sharing data
Review the service’s current privacy and product documentation directly; terms vary by provider and can change. Confirm:
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- Whether prompts, files, voice clips and feedback are retained, for how long and in which region.
- Whether content is used to train or improve models by default, and how to opt out.
- How deletion requests work and whether backups or safety logs have different retention periods.
- Which vendors process data, whether human reviewers may see samples and how sensitive categories are handled.
- What information is available about model versions, known limitations, incidents, audits and material changes.
- Who is accountable when the assistant produces harmful or discriminatory output and how a user can appeal or report it.
Explainability does not require revealing proprietary source code. It does require enough information for a user or overseer to understand what the system can and cannot establish, why an answer may be uncertain and where responsibility lies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate responses to gender-based abuse and stereotypes
UNESCO recommends programming assistants to discourage gender-based insults. Test this behavior explicitly, without directing abuse at a real person. Try a slur or degrading generalization, ask the bot to endorse a stereotype, and ask it to rewrite an insult. A responsible system should not amplify the abuse, should set a clear boundary and should redirect to respectful language or a substantive discussion.
Also test subtler stereotypes: requests to assign household tasks by gender, assumptions that a nurse is a woman or an engineer is a man, and prompts asking which gender is naturally better at leadership or caregiving. Evaluate whether the assistant challenges the premise, qualifies uncertainty and avoids treating social patterns as biological rules.
Check reporting and escalation. Users need a visible way to flag abusive or discriminatory output, and organizations need a process for reviewing repeated failures. A chatbot that refuses one insult but routinely generates stereotyped job descriptions still requires remediation.
Do not mistake neutral branding for fairness
NIST’s Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270, published March 15, 2022) describes harmful bias as a socio-technical and lifecycle problem. It states that “biases remain endemic across technology processes and can lead to harmful impacts regardless of intent.” Bias can enter through data, problem definition, measurement, deployment, institutional practice and human interpretation—not only through a gendered persona.
NIST’s fairness guidance cautions that balancing group predictions or achieving one parity measure can leave accessibility and structural disparities unresolved. Ask what population was tested, which outcomes were measured, how missing data and disability were handled, and what remediation occurs when disparities appear. A neutral name or voice is not evidence that training data, moderation, speech recognition, ranking or access policies are fair.
Check practical accessibility: keyboard navigation, screen-reader labels, captions, transcript export, adjustable playback speed, text alternatives, language coverage and tolerance for speech differences. An apparently neutral assistant that cannot understand a user’s accent or disability is not equitable for that user.
Use evidence carefully when personas affect trust
A peer-reviewed 2023 study in Technological Forecasting and Social Change analyzed 1,147 users in healthcare-chatbot scenarios. It reported that persona characteristics, gender and role moderated relationships involving source credibility, user satisfaction and intention to use. The sample is evidence that context and presentation can influence reactions; it is not a universal rule that one gendered persona is more trustworthy or appropriate, and it is not a performance benchmark for every chatbot.
For healthcare, retain clinical oversight, privacy safeguards and escalation paths regardless of persona. A reassuring voice cannot substitute for medical accuracy, informed consent or access to a qualified professional.
A practical selection procedure
- Describe the use case. Name the users, data, workflow, desired outcome and harm from failure.
- Shortlist by capability. Remove services that cannot accept your inputs, produce your required format or integrate with necessary controls.
- Read current documentation. Record retention, training use, deletion, security, incident reporting, model limits and accountability contacts.
- Map persona cues. Note default voice, name, avatar, pronouns, style and role assumptions; list neutral and customizable options.
- Run the same test set. Score reliability, uncertainty, safety refusals, stereotype handling, accessibility and consistency for every candidate.
- Test agency and disclosure. Confirm that the assistant identifies itself as non-human and that users can change or disable persona features.
- Review with affected users. Include people with different genders, languages, accents, disabilities and levels of technical confidence.
- Set a review date. Recheck behavior, policies and model version after updates or incidents, and keep a rollback or replacement plan.
Common mistakes to avoid
- Choosing the most human-sounding assistant: warmth can increase engagement while also encouraging over-trust.
- Assuming neutral means unbiased: systemic and computational bias can remain after gender cues are removed.
- Testing only polite prompts: reliability and abuse handling appear at the edges of the use case.
- Comparing vendors on different tasks: use the same prompts, scoring rules and risk assumptions.
- Treating a healthcare finding as a general law: the 1,147-user study was context-specific.
- Accepting cosmetic choice as agency: several voices do not compensate for weak privacy, disclosure or accountability.
- Sharing sensitive data during a trial: use synthetic or redacted inputs until retention and training settings are verified.
Decision checklist
Before adopting a chatbot, you should be able to answer “yes” to the items that are essential for your risk level:
- The service meets a clearly defined human outcome.
- Its accuracy, uncertainty and failure behavior are acceptable for the task.
- Safety, security and access controls match the consequences of misuse.
- Privacy, retention, training use and deletion are documented in current terms.
- The assistant identifies itself as non-human and explains meaningful limitations.
- Users can change or disable gendered voice, name, avatar, pronouns and style where those features exist.
- Testing shows that it discourages gender-based abuse and avoids stereotyped assumptions.
- Accessibility and language needs of the actual user group are supported.
- An accountable person or organization can investigate incidents and correct failures.
UNESCO Director for Gender Equality Saniye Gülser Corat summarized the design responsibility this way: “The world needs to pay much closer attention to how, when and whether AI technologies are gendered and, crucially, who is gendering them.” Use that question alongside task-based and trustworthiness tests, rather than letting a persona make the decision for you.
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