AI Ethics Policy Template

Why You Need an AI Ethics Policy

An AI ethics policy is the higher-level companion to an internal AI use policy. Where the use policy says "what tools are allowed for what data", the ethics policy says "what principles guide all our AI work, customer-facing and internal." Customers, regulators, employees, and investors increasingly ask to see one. This template is structured to be useful within 60 minutes of adaptation.

Section 1: Our AI Principles

1. Human oversight. Every consequential AI decision has a named human reviewer.
2. Transparency. We disclose meaningful AI use to customers, employees, and partners.
3. Fairness. We test AI systems for disparate impact across protected classes before deployment and at recurring intervals.
4. Safety. We do not deploy AI in high-stakes contexts (safety-of-life, irreversible financial harm) without redundant safeguards.
5. Privacy. Personal data flows only into AI systems that meet our data-handling tier requirements.
6. Accountability. A named owner is accountable for each AI system in production.

Section 2: Prohibited Uses

• Generating deepfakes of real people without explicit consent.
• Using AI to make hiring, firing, promotion, or compensation decisions without human review.
• Using AI to evaluate or surveil customers, employees, or third parties in ways that violate applicable law.
• Using AI to generate intentionally misleading or deceptive content.
• Using AI in any product or process intended to harm individuals, groups, or critical infrastructure.
• Selling or sharing AI outputs in violation of upstream model terms of service.

Section 3: Fairness and Bias

1. For any AI system that affects access to opportunity (hiring, lending, housing, education, healthcare): we measure disparate impact before launch and quarterly thereafter.
2. If measured disparate impact exceeds a defined threshold, the system is paused and reviewed.
3. Bias-testing methodology and results are documented and available to internal audit.
4. External AI vendors must provide their bias-testing methodology and results before procurement.
5. Training data is reviewed for representativeness; corrective actions documented.

Section 4: Transparency and Disclosure

• Customer-facing AI features are labelled as such.
• AI-generated content meeting our disclosure threshold carries a public-facing disclosure.
• AI use in hiring, performance review, or termination decisions is disclosed to affected employees.
• AI use in customer support is disclosed at the start of the conversation when material to the user experience.
• We publish an annual AI transparency report describing model use, governance, incidents, and outcomes.

Section 5: Human Oversight

1. High-stakes decisions (any decision that materially affects health, safety, finances, employment, or legal status) require a named human reviewer.
2. AI cannot be the sole decision-maker for any consequential customer-facing action.
3. Customers have a right to escalate any AI decision to a human reviewer.
4. Internal teams document the human-review process for each AI-supported decision class.

Section 6: Vendor and Model Selection

• External AI vendors must meet our data residency, privacy, and security requirements.
• We prefer vendors with published responsible AI policies and incident histories.
• Open-weights models are evaluated against the same criteria as proprietary APIs.
• Vendor risk is reviewed at least annually for all AI systems in production.

Section 7: Incident Response

1. AI incidents (hallucinations causing harm, bias incidents, data leaks, safety failures) are reported within 24 hours to the AI Governance Committee.
2. Customer-affecting incidents are communicated to affected customers within timelines required by applicable law.
3. Post-incident reviews are conducted and findings drive policy updates.
4. Repeat incidents trigger formal remediation plans with executive accountability.

Section 8: Governance and Review

1. An AI Governance Committee (CTO, CISO, Legal, HR, Product lead, Customer Trust lead) owns this policy.
2. The policy is reviewed quarterly and updated as needed.
3. Material changes are communicated to all employees within five business days.
4. Annual external review by an independent advisor is recommended for companies above 1,000 employees or in regulated industries.

Adoption Checklist

1. Adapt the principles to your specific values and industry context.
2. List your actual prohibited uses (the example list is starting point, not final).
3. Define disparate-impact thresholds with help from a quantitative team.
4. Stand up the AI Governance Committee.
5. Publish in the employee handbook and on a public ethics page.
6. Train customer support and HR teams on the disclosure and escalation paths.