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A respectful AI use policy does more than tell people which buttons they may press. It explains why the organization uses AI, protects the dignity and rights of people affected by it, limits uses according to risk, keeps a human accountable for consequential decisions, protects information throughout the AI lifecycle, and gives people a practical way to question or challenge outcomes.
The most reliable way to write one is to inventory actual tools and use cases first, classify them by likely impact, then set concrete permissions, safeguards, review duties, training, and update procedures. The policy should be understandable to employees, students, contractors, customers, and other affected people—not only to the technical team.
What a respectful AI policy must accomplish
Begin with a short purpose statement that connects AI use to the organization’s mission and its responsibilities to people. Name the interests the policy protects: human dignity, equal treatment, privacy, security, accessibility, and accountable judgment. Include people who may be affected by an AI-assisted process even if they never operate the system.
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Write for the people who bear the consequences
List the groups your systems may affect: employees and applicants, students and families, customers and service users, vendors, visitors, and members of the public. For each group, consider what an error, biased output, disclosure, or unexplained decision could cost them. This keeps the policy from becoming a narrow acceptable-use memo for staff.
Use proportionality instead of a blanket ban or blanket permission
UNESCO states that “The use of AI systems must not go beyond what is necessary to achieve a legitimate aim.” Translate that principle into a practical question: is AI the least intrusive and reasonably effective way to accomplish the stated purpose? A low-risk drafting assistant and a system that ranks job applicants should not be governed by identical rules.
Start with an inventory of tools and use cases
Do not draft detailed permissions from a list of product names alone. The same tool can be harmless for brainstorming and high-impact when its output influences admission, discipline, employment, credit, health, housing, benefits, or access to services. Create an inventory before deciding what is allowed.
Record the minimum facts for every use case
- Purpose: what task or outcome the system supports and what legitimate objective it serves.
- Tool and version: the product, embedded feature, model or service, account type, and material configuration.
- Users: which roles operate it and what training they receive.
- Inputs: the categories of data entered, including personal, confidential, sensitive, regulated, or proprietary information.
- Outputs: what the system produces, how it is stored, and who sees it.
- Affected people: everyone who may be evaluated, contacted, excluded, monitored, or otherwise influenced.
- Decision impact: whether the output is merely assistive or can change access, opportunity, evaluation, safety, privacy, or rights.
- Owner: the person or team accountable for the use case, review, incidents, and updates.
- Controls: access restrictions, retention settings, human checks, testing, logging, and escalation routes already in place.
The U.S. Equal Employment Opportunity Commission’s 2024 compliance plan for OMB Memorandum M-24-10 describes a practical model: review software inventories to find AI use cases, use an AI questionnaire, apply assessment templates, and involve people from multiple functions. Adapt that process to your organization rather than copying an agency form.
Include AI hidden inside ordinary software
Scope language should cover AI features embedded in office suites, help desks, recruiting systems, learning platforms, customer-service tools, security products, and analytics dashboards. Otherwise employees may assume that an AI feature is automatically approved because the surrounding application was purchased through normal procurement.
Classify uses by risk and decision impact
A short tiering table makes a policy easier to apply than a long list of abstract principles. The names below are a drafting tool, not a legally mandated taxonomy. Adjust thresholds to your sector, jurisdiction, and actual processes.
| Tier | Typical use | Minimum rule | Required record |
|---|---|---|---|
| Low impact | Formatting, translation drafts, brainstorming, code comments, meeting summaries that do not determine a person’s rights or opportunities | Use an approved tool, exclude restricted data, verify the output before sharing, and disclose AI assistance when your audience needs to know | Owner, tool, purpose, and basic data check |
| Moderate impact | Internal analysis, customer communications, tutoring suggestions, workflow prioritization, or recommendations that staff may rely on | Complete a documented review, define quality checks, monitor error patterns, and require a named human reviewer before action | Use-case assessment, reviewer, data sources, tests, and escalation path |
| High impact | Hiring, promotion, discipline, student evaluation, eligibility, access to services, safety decisions, surveillance, or outputs involving sensitive traits | Obtain formal approval before deployment, use a validated and appropriate method, provide meaningful human review, document reasons, and offer a route to challenge the result | Risk assessment, legal and privacy review where applicable, testing evidence, decision owner, notices, and incident plan |
| Prohibited or suspended | Uses that lack a legitimate purpose, evade required safeguards, expose restricted information to an unapproved service, or cannot be fairly and safely reviewed | Do not deploy or stop use until the owner and designated reviewers resolve the issue | Reason for prohibition or suspension and authorization required to resume |
Describe the factors that move a use into a stricter tier: the sensitivity and volume of data, scale, reversibility of harm, effect on a person’s opportunities or rights, likelihood of bias, security exposure, and how much a human can realistically check the result.
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Approved uses
Give concrete examples rather than saying “AI may be used responsibly.” An approved-use section might allow drafting a non-sensitive internal outline, converting a staff-written document into plain language, generating test data that contains no real personal information, or suggesting code that a qualified developer reviews. Approval should still depend on the organization’s tool inventory and data rules.
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Uses requiring review or approval
Require review before AI output is used to rank, recommend, screen, score, monitor, or communicate a consequential decision about a person. Include recruitment and performance management, admissions and grading, benefits or service eligibility, fraud or safety flags, health-related support, and any process that could affect legal rights or material access. State who approves the use and what evidence they need.
Prohibited uses
Prohibit uses that have no legitimate organizational purpose, intentionally discriminate, impersonate a person without authorization, create deceptive evidence, bypass a required human decision, or expose protected information through an unapproved tool. Prohibit entering secrets, credentials, confidential case details, regulated records, or identifiable personal information unless the tool and processing have been specifically approved for that category.
Explain the reason for each prohibition. People comply more reliably when they understand that a rule prevents a concrete privacy, security, fairness, or accountability failure.
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UNESCO states that “Privacy must be protected and promoted throughout the AI lifecycle.” A policy should cover collection, prompting, transmission, storage, access, reuse, retention, deletion, monitoring, and disposal—not only the moment a user enters a prompt.
Define data categories users can recognize
- Public: information already approved for public release.
- Internal: routine non-public material that an approved service may process under organizational controls.
- Confidential: business, contractual, personnel, student, customer, or partner information restricted to authorized people.
- Sensitive or regulated: information subject to privacy, health, financial, education, employment, security, or other legal or contractual obligations.
- Restricted secrets: passwords, private keys, access tokens, security findings, unreleased credentials, and similar material that must never be entered into a general AI service.
Map each category to an action: allowed in named approved tools, allowed only after de-identification, allowed only with a documented review, or forbidden. Do not promise that a service is private, secure, or non-retained unless the organization has verified the relevant contract and settings.
Apply data minimization
Tell users to provide only the information needed for the task, remove names and direct identifiers when possible, and avoid copying an entire case file when a short excerpt will do. Require access controls that match the sensitivity of prompts and outputs. Define retention and deletion expectations for logs, generated files, and exported results.
Plan for incidents
Give a single, visible route for reporting an accidental disclosure, suspicious output, compromised account, or unauthorized use. State what the reporter should preserve—such as the prompt, output, date, tool, and recipients—without asking them to circulate additional sensitive data. The responsible security, privacy, or compliance team should determine containment, notification, and remediation duties under applicable law and contracts.
Keep meaningful human oversight and accountability
UNESCO says, “Member States should ensure that AI systems do not displace ultimate human responsibility and accountability.” NIST’s trustworthiness characteristics add validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed.
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Define what the reviewer must do
“Human in the loop” is not enough if the person simply accepts a machine score. Specify that the reviewer must:
- check whether the output addresses the actual question and uses appropriate information;
- look for factual errors, missing context, stereotyping, discriminatory patterns, and unsafe recommendations;
- compare important claims with authoritative records or source material;
- consider reasonable alternatives and any information the system could not see;
- record the basis for a consequential decision in ordinary professional terms; and
- stop, escalate, or seek a second review when confidence is low or the case is unusual.
Assign a named decision owner. “The AI recommended it” is neither an explanation nor a defense. The owner remains responsible for the decision, the quality of the process, and correcting errors.
Set verification standards by impact
For low-impact drafting, a quick accuracy and confidentiality check may be sufficient. For high-impact uses, require documented testing, representative data where lawful, review of error rates and disparate effects, a qualified reviewer, and a record of the final human reasoning. If the organization cannot perform a meaningful check, it should not rely on the output for that decision.
Make decisions understandable and contestable
People affected by an AI-assisted process need an appropriate way to learn that AI was used, correct inaccurate information, request human review, or report harm. The exact notice and appeal design depends on the sector, jurisdiction, sensitivity, and security needs; no single procedure fits every organization.
Write a usable explanation
Explain the system’s role, the type of information considered, the human decision-maker, and the practical next step for questions. Avoid revealing private information about another person, exploitable security details, or trade secrets. A useful explanation describes the process and the relevant reasons without pretending that an automated score is an objective fact.
Provide a route to correction
Publish an email address, form, case-management route, or supervisor contact that people can actually use. Set a response owner and target time appropriate to the decision’s urgency. Allow a person to submit corrected information and have a human reconsider the outcome when the issue could affect access, opportunity, safety, privacy, or rights. Record the complaint, investigation, resolution, and any change to the system or policy.
Assign governance roles
A policy fails when everyone is “responsible” in theory and no one owns a decision in practice. Name roles and substitute your organization’s titles:
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| Executive sponsor | Sets the organization’s aims, accepts material risk, and ensures resources for oversight. |
| Use-case owner | Describes the purpose, maintains the inventory entry, monitors performance, and stops use when controls fail. |
| Privacy and security reviewers | Assess data handling, access, retention, contracts, threat exposure, and incident response. |
| Legal, compliance, or ethics reviewers | Check applicable obligations, fairness concerns, notices, procurement terms, and records requirements. |
| Technical reviewer | Evaluates configuration, testing, reliability, logging, model or vendor changes, and integration risk. |
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| Training owner | Delivers role-specific learning, measures effectiveness, and updates materials when tools or rules change. |
For small organizations, one person may hold several roles, but the policy should still separate approval from day-to-day use where a conflict could arise.
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A step-by-step drafting process
- Write the purpose and protected interests. State why the organization uses AI and its commitments to dignity, equal treatment, privacy, security, accessibility, and accountable human judgment.
- Set the scope. Include employees, contractors, temporary staff, students, vendors, embedded features, internally built systems, and external services where relevant.
- Build the inventory. Record each tool, use case, data category, affected group, impact, owner, and existing control.
- Classify risk. Use purpose, reversibility, scale, sensitivity, opportunity or rights impact, bias risk, and reviewability to assign a tier.
- Draft concrete permissions. List approved examples, review triggers, prohibited actions, disclosure expectations, and the process for requesting an exception.
- Set data rules. Define categories, approved services, de-identification expectations, access, retention, deletion, and incident reporting.
- Design human review. Identify the reviewer, verification steps, documentation, escalation conditions, and final decision owner for each moderate- or high-impact use.
- Design notice and challenge routes. Decide when people are told AI was used, how they can correct information, who conducts reconsideration, and how complaints are tracked.
- Obtain cross-functional approval. Have operational, technical, privacy, security, legal or compliance, accessibility, and affected-community perspectives where appropriate.
- Train before launch. Require role-specific instruction and an acknowledgement or competency check before access to higher-risk tools.
- Publish an operational version. Put the policy, approved-tool list, reporting route, and request forms where users work. A short quick-reference page can sit alongside the full policy.
- Monitor and revise. Review incidents, complaints, accuracy findings, tool changes, new use cases, and legal developments; document each revision and its effective date.
Choose a structure people can follow
No cited source requires one universal document format. UNESCO’s education guidance describes independent, integrated, and thematic approaches for education policy responses; those are options for education policy design, not a taxonomy that every employer or nonprofit must adopt. Compare your choices against usability and governance needs.
| Structure | Best fit | Strength | Watch for |
|---|---|---|---|
| One organization-wide policy with appendices | Organizations that need one baseline and a manageable approval route | Consistent definitions, reporting, and accountability | Can become too general unless team-specific examples and controls are attached |
| Risk- or use-case-based rules | Organizations with varied applications and different levels of impact | Users see the permission and review steps for their actual task | Requires a maintained inventory and clear tier definitions |
| Topic-based rules | Organizations whose main concerns are privacy, security, disclosure, or procurement | Maps naturally to existing control owners | Users may miss the combined risk of a particular use case |
| Team or sector supplements | Schools, hospitals, public bodies, or enterprises with distinct duties | Allows precise examples and legal or professional context | Conflicting supplements can confuse users unless one baseline policy prevails |
| Living policy with a short core and linked procedures | Fast-changing environments with many tools | Core commitments remain stable while procedures and approved-tool lists can change quickly | Links, owners, version dates, and archive practices must be maintained |
Whatever structure you choose, make ownership, approval thresholds, data rules, and update duties easy to find. A policy that cannot be located or understood is not an effective control.
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Training should cover permitted uses, restricted data, prompt and output limitations, verification, disclosure, security, accessibility, bias awareness, human-review duties, and escalation. A developer, recruiter, teacher, customer-support agent, executive, and privacy reviewer need different exercises even when they read the same baseline policy.
NIST SP 800-50r1, Building a Cybersecurity and Privacy Learning Program, presents a customizable lifecycle: plan the program, deliver role-appropriate learning, measure whether it works, and improve it. Apply that approach here. Track completion, but also test behavior through scenario exercises, short assessments, quality reviews, incident trends, and feedback from people who use the policy.
Use realistic scenarios
- An employee pastes a customer complaint containing identifying details into a public chatbot.
- A manager receives an AI-generated ranking of applicants and wants to reject everyone below an unexplained threshold.
- A teacher uses an AI detector as the sole basis for discipline.
- A staff member notices that a generated translation changes the meaning of a safety instruction.
- A person affected by an automated eligibility recommendation asks for the data and reasoning used.
Require learners to identify the data issue, risk tier, human reviewer, verification step, and reporting route in each scenario.
Maintain the policy as a working control
Set a review trigger and an ordinary review cycle. Trigger an out-of-cycle review when a new high-impact use is proposed, a vendor or model changes materially, an incident occurs, a complaint reveals a gap, data categories expand, or applicable obligations change.
The EEOC’s published process describes ongoing inventory and evaluation work and gives that agency a minimum two-year review cycle. Treat that interval as an agency-specific example, not a universal rule. Your cycle should be short enough for the pace of tool and use-case changes and long enough to support a meaningful review.
Keep records that prove the policy is operating
- current inventory and owner for every use case;
- approval, assessment, and procurement records;
- training assignments, completion, and effectiveness measures;
- testing, monitoring, and quality findings;
- incidents, complaints, corrections, and response times;
- policy versions, effective dates, and change reasons; and
- retirement or suspension decisions and conditions for resuming use.
UNESCO also states that “AI systems should be auditable and traceable.” Keep enough evidence to reconstruct what system was used, for what purpose, with what data and human review, without retaining more personal information than necessary.
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Common drafting failures and how to fix them
Failure: a vague command to use AI responsibly
Fix: replace it with examples, data categories, approval triggers, reviewer duties, and a reporting route.
Failure: treating every AI use as equally dangerous
Fix: classify by purpose and impact. Proportionate controls are easier to follow and leave attention for consequential uses.
Failure: banning public tools while ignoring embedded features
Fix: inventory AI capabilities inside ordinary software and apply the same data and review rules.
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Failure: calling a person a reviewer without giving them authority or time
Fix: name the decision owner, define verification steps, require escalation, and make clear that the person may reject the output.
Failure: promising confidentiality without checking contracts and settings
Fix: identify approved services and verified controls; otherwise prohibit the relevant data category.
Failure: publishing a policy with no way to challenge an outcome
Fix: provide notice, correction, human reconsideration, a response owner, and a record of resolution where AI affects a person.
Failure: measuring training only by attendance
Fix: test decisions with scenarios, review real outputs, monitor incidents, and revise learning when behavior does not improve.
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Failure: treating one jurisdiction’s rule as universal
Fix: have qualified advisers verify requirements for the organization’s location, sector, workforce, data, and publication date. U.S. Executive Order 14110 included worker participation, employment quality, civil-rights, privacy, and training considerations in federal policy, but its current legal status and applicability should be checked before relying on it.
A compact policy outline you can adapt
The following outline keeps the document operational while leaving room for sector-specific procedures:
- Purpose and commitments: dignity, rights, fairness, inclusion, privacy, security, accessibility, transparency, and accountable human judgment.
- Scope and definitions: AI, generative AI, automated decision support, approved tool, personal data, confidential data, high-impact use, and human review.
- Inventory and ownership: where the register is kept and who maintains each entry.
- Risk tiers: criteria, examples, approval authorities, and reassessment triggers.
- Permitted and prohibited uses: concrete tasks and data examples.
- Data and security controls: minimization, approved services, access, retention, deletion, and incidents.
- Human oversight: reviewer qualifications, verification, documentation, escalation, and final accountability.
- Transparency and challenge: notices, explanations, corrections, human review, and complaint handling.
- Training: required audiences, timing, scenarios, and effectiveness measures.
- Governance and maintenance: committees or owners, records, audits, review cycle, version control, and suspension criteria.
- Contacts and procedures: links or addresses for approval requests, privacy questions, security incidents, and reports of harm.
Have local counsel, privacy and security specialists, accessibility advisers, labor or student representatives, and operational leaders review the draft when their responsibilities or rights are implicated. The policy should describe what your organization can actually enforce, not an ideal process that no team has the authority or resources to operate.
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