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A respectful AI use policy does more than ask people to be polite. It sets proportionate limits, protects personal and confidential information, keeps a human accountable for consequential decisions, and gives affected people a way to question or challenge an outcome. Start with the organization’s real tools and use cases, then write plain-language rules that explain what is allowed, what needs review, and what is forbidden.
What a respectful AI use policy must accomplish
Write the policy as a governance document, not a statement of enthusiasm or fear. It should help a person decide what to do before entering a prompt, relying on an output, or using AI in a decision about someone else. The document should identify the people who may be affected—including employees, students, customers, applicants, patients, residents, contractors, and members of the public—not only the staff operating the system.
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UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted by 193 Member States in November 2021, places human dignity, human rights, fairness, inclusion, transparency, human oversight, privacy, and accountability at the center of AI governance. Its proportionality principle is direct: “The use of AI systems must not go beyond what is necessary to achieve a legitimate aim.” That principle gives a practical test for every rule in your policy: identify the legitimate purpose, then prohibit collection, automation, or inference that exceeds what that purpose requires.
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A courteous tone cannot compensate for an unaccountable process. A policy that says “use AI responsibly” but permits an unreviewed model to reject an applicant, grade a student, or deny a service is not respectful. Respect is demonstrated through limits, explanations, recourse, and named responsibility.
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1. State the purpose and the people you protect
Open with a short purpose statement that connects AI use to the organization’s mission and its obligations to people. Explain the benefits the organization seeks—such as drafting, translation, accessibility, research support, or administrative efficiency—without implying that efficiency overrides rights or safety.
Name the commitments the policy makes. A useful statement covers:
- human dignity and equal treatment;
- privacy, confidentiality, and information security;
- accurate, reliable, and explainable work;
- meaningful human judgment for consequential matters;
- accessibility and inclusion;
- a way to ask questions, correct information, or report harm; and
- regular review as tools and uses change.
Say who the policy covers: employees, temporary staff, contractors, volunteers, students, vendors acting for the organization, and anyone else who can access an approved AI system. State whether the rules apply to public chatbots, privately managed models, AI features embedded in ordinary office software, automated scoring, image or audio generators, and systems supplied by a third party. If a category is outside scope, explain which separate rule governs it.
2. Inventory tools and use cases before writing permissions
Do not begin with a generic list of allowed prompts. First create an inventory of the AI actually used or proposed. The U.S. Equal Employment Opportunity Commission’s 2024 compliance plan for OMB Memorandum M-24-10 describes an agency process that reviews software inventories, identifies AI use cases, and uses questionnaires and assessment templates. You can adapt that process to any sector.
For every tool or feature, record:
- the product or system name, version or configuration, and owner;
- the business or educational purpose;
- the people who operate it and the people affected by its output;
- the information entered, generated, retrieved, or retained;
- whether a vendor or another service receives the data;
- the action a person might take based on the output;
- the consequences of an error, bias, outage, or data disclosure;
- the required reviewer and escalation contact; and
- the date and trigger for the next review.
Include “ordinary” software. A writing suite, customer-support platform, recruiting system, learning-management system, camera, or security product may add an AI feature without a separate purchase decision. If the feature can summarize messages, rank people, infer characteristics, generate content, or recommend an action, it belongs in the inventory.
Use an owner for every entry
Assign one accountable owner even when several teams use a system. The owner coordinates privacy and security checks, maintains the approved-use description, records incidents, and confirms that required reviewers are trained. Shared responsibility is useful for consultation, but “everyone” is not an accountable person.
3. Define plain-language terms and a proportional risk model
Define only terms that readers need in order to follow the rules. Explain “AI system” as software that generates, predicts, recommends, ranks, classifies, or otherwise produces an output from data. Define “personal information,” “confidential information,” “sensitive information,” “regulated information,” “human review,” and “consequential decision” using categories that match your organization and jurisdiction.
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| Use level | Typical purpose | Minimum policy treatment |
|---|---|---|
| Routine assistance | Brainstorming, formatting, transcription, translation, or summarizing material that contains no restricted information | Permit with approved tools, user verification, and disclosure when the context requires it |
| Internal support | Drafting internal documents or analyzing organizational data that is not highly sensitive | Require an approved environment, data-minimization controls, and a named owner |
| Impacting people | Recommendations or classifications that may affect hiring, evaluation, education, eligibility, service access, pay, safety, or reputation | Require documented risk assessment, qualified human review, an explanation appropriate to the context, and a route to challenge the result |
| High-consequence or prohibited | Fully automated decisions about rights or essential opportunities, or uses involving unlawful discrimination, deception, surveillance without authority, or unsafe instructions | Prohibit unless a separately authorized process establishes necessity, safeguards, oversight, and legal compliance; some uses should remain forbidden |
UNESCO calls for risk assessment and says AI use should not exceed what is necessary for a legitimate aim. Put that test into the approval form: What is the aim? Why is AI needed? What less intrusive method was considered? What could go wrong, and how will the organization detect and remedy it?
4. Write the rules users will actually follow
Approved uses
List concrete tasks rather than saying “low-risk use is allowed.” Examples include turning a user-written outline into a first draft, suggesting neutral headings, translating a public document for a human to check, producing code for a sandbox, or summarizing a meeting after confidential details have been removed. State any conditions beside each task: approved account, no personal data, human fact-checking, accessibility check, or disclosure to the recipient.
Uses requiring approval or additional review
Require review before an AI output can influence hiring, promotion, discipline, admission, grading, benefits, credit, housing, insurance, health or safety, access to services, or any other opportunity or right. Also require review for biometric identification, emotion or personality inference, monitoring of workers or students, profiling of vulnerable people, automated content moderation that can remove a person’s access, and generation of advice that could create physical, financial, or legal harm.
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The approval record should identify the purpose, data, affected groups, model or feature, validation method, reviewer, retention period, user notice, appeal route, and stop conditions. Approval is not permanent: a changed model, new data source, new population, or new decision can require a fresh assessment.
Prohibited uses
Prohibit uses that violate law or policy, deliberately deceive people about whether they are interacting with a person, expose restricted information to an unapproved service, generate instructions for wrongdoing, or make a consequential decision with no meaningful human involvement. Do not frame a prohibition so broadly that ordinary assistance becomes impossible; identify the harm and the boundary in plain language.
5. Protect privacy, confidentiality, and security
Tell users exactly what they may not paste, upload, dictate, or connect to an AI tool. Categories commonly requiring special handling include personal identifiers, health and biometric information, financial or payment data, authentication secrets, student or employee records, information covered by professional privilege, confidential business plans, security details, and data obtained under a contract or consent that limits reuse.
Use a simple rule when classification is uncertain: stop and ask the information owner or privacy contact before submitting it. Do not promise that a tool is private, secure, or excluded from training unless the organization has verified those terms for the exact account and configuration.
Require data minimization. Remove names, account numbers, exact dates, faces, locations, and other identifying details when the task can be completed without them. Use the least amount of data and the shortest retention period that meets the purpose. Restrict access by role, record who can retrieve prompts and outputs, and define how to delete or archive them. Security controls should cover credentials, integrations, exports, logging, vendor access, and incident response.
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UNESCO states, “Privacy must be protected and promoted throughout the AI lifecycle.” NIST’s trustworthiness characteristics likewise include privacy and security, alongside validity and reliability, fairness, accountability and transparency, explainability and interpretability, and safety. Treat those characteristics as design and operating checks, not marketing labels.
6. Make human review meaningful
“A human in the loop” is not meaningful if the person merely clicks approve. Name the reviewer, give them enough time and authority to reject the output, and describe what they must verify. Depending on the use, checks may include factual accuracy, missing context, bias, accessibility, source quality, privacy, security, and whether the recommendation is necessary and proportionate.
Set stop and escalation conditions. A reviewer should pause the process when the output is inconsistent, cites sources that cannot be verified, uses sensitive attributes, conflicts with a person’s account, or could cause serious harm. Escalate to a qualified specialist for legal, medical, safeguarding, employment, academic-integrity, or security questions.
Assign responsibility for the final decision to a role with authority to correct it. “The AI recommended it” is neither an explanation nor an excuse. UNESCO’s Recommendation says, “Member States should ensure that AI systems do not displace ultimate human responsibility and accountability.” It also says, “AI systems should be auditable and traceable.” Keep records sufficient to reconstruct which system was used, what input and version mattered, who reviewed the output, what decision was made, and what correction followed.
7. Explain use and provide a route to challenge harm
When AI materially influences an outcome about a person, tell that person that AI was used when disclosure is appropriate and lawful. Explain the system’s role in ordinary language: what it considered, what it did not determine, and who made the final decision. Do not expose another person’s private information, security-sensitive details, or trade secrets in the name of transparency.
Provide a practical contact route. It should allow an affected person to:
- ask whether and how AI was used;
- point out inaccurate or incomplete information;
- request review by a human with authority to change the result;
- ask for an accommodation or accessible format; and
- report suspected discrimination, privacy loss, security incidents, or other harm.
Set response targets, preserve relevant records, and tell staff how to escalate urgent safety or safeguarding concerns. The sources do not prescribe one universal appeal procedure, so design the route around the decision, the people affected, and applicable law.
8. Build accountability into governance
Define a small governance structure that can make decisions and resolve conflicts. A workable allocation is:
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- Executive sponsor: approves the policy and supplies authority and resources.
- System owner: maintains the inventory entry, use description, controls, and review dates.
- Privacy and security leads: assess data handling, access, retention, and incident response.
- Subject-matter reviewer: tests accuracy, fairness, accessibility, and domain risks.
- Front-line manager: confirms that staff follow the approved workflow.
- Independent escalation contact: receives complaints and can require a pause or re-review.
Document decisions and exceptions. An exception should have a reason, scope, expiration date, compensating controls, and approving authority. Keep an incident log that records what happened, who was affected, containment, notification, root cause, remedy, and whether the policy or training changed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Choose a structure people can navigate
No single document structure is required by UNESCO, NIST, or the EEOC. Choose one that matches how your organization works, then make ownership and update duties visible.
| Structure | How it works | Useful when | Main design risk |
|---|---|---|---|
| Organization-wide core plus team rules | A short common policy sets non-negotiable principles; teams add procedures for their tools and decisions | Many departments share basic duties but face different data and risk | Local rules can conflict unless one owner controls versioning |
| Risk and use-case based | Rules are grouped by routine assistance, internal support, people-impacting use, and prohibited use | Users need a quick “which path applies?” decision | Readers may miss cross-cutting privacy or security requirements |
| Topic based | Separate sections cover privacy, security, fairness, disclosure, review, and training | Specialist teams maintain distinct controls | A user may have to read several sections before finding a permission |
| Lifecycle based | Requirements follow proposal, procurement, testing, deployment, monitoring, incident, and retirement | Formal technology governance and frequent system changes | Front-line users may not find immediate task guidance |
A hybrid is often practical: a concise organization-wide policy, a risk decision tree, and appendices for data handling, approved tools, review forms, and incident contacts. Whichever structure you choose, publish a version number, effective date, owner, change log, and next review trigger.
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Training should cover permitted tasks, restricted data, prompt and output risks, verification, disclosure, accessibility, security, incident reporting, and how to obtain approval. A general introduction is not enough for a recruiter, teacher, developer, caseworker, researcher, or system administrator who faces different risks.
Use realistic exercises: remove identifying details from a sample prompt, find unsupported claims in an AI summary, compare an output with a primary record, respond to a biased recommendation, and report a simulated data exposure. Require a short acknowledgement or assessment where the risk justifies it, but measure behavior rather than attendance alone.
NIST SP 800-50r1, Building a Cybersecurity and Privacy Learning Program (2024), presents a customizable lifecycle for planning, delivering, measuring, and improving organizational learning. Apply that cycle: identify role needs, set learning objectives, deliver training, measure comprehension and incidents, then revise the content.
11. Test, monitor, and update the policy
Set review triggers instead of relying only on a calendar. Reassess when a model or vendor changes, a new data source or population is introduced, a use expands from drafting to decision support, an incident occurs, a law or contract changes, or monitoring shows unequal error or complaint patterns.
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- number and type of approved, rejected, and exceptional uses;
- privacy or security incidents and near misses;
- corrections to AI-assisted work;
- complaints, appeals, accommodations, and response times;
- reviewer overrides and reasons;
- completion and effectiveness of role-based training; and
- performance differences across relevant groups where measurement is lawful and appropriate.
The EEOC’s agency example describes ongoing inventory and evaluation work and a minimum two-year review cycle for that agency. That interval is an example, not a universal rule. Your policy should specify a shorter interval when tools or risks change quickly, and it should allow an immediate review after a serious incident.
12. A drafting and approval workflow
- Collect facts. Interview users, affected groups, privacy and security staff, procurement, legal advisers, accessibility specialists, and worker or student representatives. Build the tool and use-case inventory.
- Map impacts. For each use, identify the purpose, data, affected people, decision impact, foreseeable harms, existing controls, and less intrusive alternatives.
- Set the boundary. Classify the use, write approved conditions, define review and escalation requirements, and state prohibitions.
- Write the user path. Put the answer to “Can I use AI for this?” near the front, with links or references to detailed procedures. Use examples that resemble real work.
- Assign owners. Name the policy owner, system owner, reviewers, complaint contact, and authority for exceptions.
- Test comprehension. Ask representative users to classify sample scenarios and find the correct contact without coaching. Rewrite unclear rules.
- Approve and publish. Record the approval date, effective date, version, scope, and change history. Provide accessible formats and a short staff summary.
- Operate and improve. Train users, collect incidents and appeals, review metrics, and update the inventory and policy when conditions change.
Common failure modes and how to fix them
One vague rule for every tool
A blanket ban may drive use into unapproved channels, while a blanket permission ignores high-impact risks. Replace it with risk and use-case tiers, approved examples, and an approval route.
Promises about vendor privacy
Marketing language or a default setting does not establish how prompts are retained, accessed, or reused. Verify the exact account and configuration; otherwise require users to omit restricted information.
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If reviewers lack authority, time, training, or relevant records, the review is nominal. Define verification duties, stop conditions, and a way to override the system.
No voice for affected people
A policy written only for operators misses errors that the affected person can see first. Add notice where appropriate, correction, human review, accommodation, and complaint channels.
Policy without maintenance
A static PDF becomes inaccurate when features, vendors, data, or laws change. Maintain an inventory, version history, review triggers, metrics, and an owner with authority to update the rules.
Final publication checklist
- The purpose names the people and values the policy protects.
- Scope includes embedded AI features and third parties.
- Tools, data, owners, affected groups, and decision impacts are inventoried.
- Terms and risk levels are understandable without specialist knowledge.
- Approved, review-required, and prohibited uses have concrete examples.
- Data minimization, access, retention, security, and incident rules are explicit.
- Human reviewers have authority, duties, stop conditions, and records to inspect.
- Accountability remains with a named role.
- Affected people can ask questions, correct information, seek review, and report harm.
- Training is role-based and evaluated for effectiveness.
- Versioning, review triggers, metrics, and an update owner are published.
- Jurisdiction- and sector-specific legal requirements have been checked before reliance.
Use this checklist as a quality gate, not as a substitute for judgment. The right policy is the one your organization can operate, explain, and improve while preserving the dignity and agency of the people its AI use touches.
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