AI Ethics Policy Template
A ready-to-customise ethics policy that establishes your organisation's commitment to responsible AI. Covers core ethical principles, bias prevention, transparency obligations, privacy protections, and accountability structures.
Overview
What's included
Policy Statement & Principles
AI Ethics Policy
Organisation: Effective date: Policy owner: Approved by:
Policy Statement
[Organisation name] is committed to developing, deploying, and using AI in ways that are ethical, fair, transparent, and beneficial to our customers, employees, and society. This policy defines the ethical standards that govern all AI activities across the organisation.
Scope
This policy applies to:
- All employees, contractors, and partners who develop, deploy, or use AI systems
- All AI models, algorithms, and automated decision-making systems
- All third-party AI services used by the organisation
Core Principles
1. Fairness and Non-Discrimination Our AI systems will not unfairly discriminate against individuals or groups. We will proactively test for and mitigate bias in our data, models, and outputs.
2. Transparency We will be open about where and how we use AI. Individuals affected by AI decisions have the right to know that AI was involved and to understand the key factors in the decision.
3. Privacy and Data Protection AI systems will process personal data only with a lawful basis. We will minimise data collection, protect data integrity, and respect individuals' rights over their data.
4. Safety and Reliability AI systems will be tested, validated, and monitored to ensure they perform as intended and do not cause harm. We will implement safeguards proportionate to the risk.
5. Accountability Every AI system has a designated owner. Humans remain accountable for AI decisions, especially those with significant impact on individuals.
6. Societal Benefit We will consider the broader impact of our AI use on employees, customers, communities, and the environment.
Bias Detection & Mitigation
Bias Detection & Mitigation
Requirements
All AI systems must comply with the following bias-related requirements:
-
Data bias assessment: Before training or fine-tuning any model, assess training data for representation bias across protected characteristics (age, gender, race, disability, religion, sexual orientation).
-
Fairness testing: Test model outputs across demographic groups before deployment. Document results and remediation actions.
-
Ongoing monitoring: Monitor production AI systems for bias drift. Set alerts for significant changes in outcome distribution.
Protected Characteristics
Our bias testing will cover, at a minimum:
- Age
- Gender
- Race and ethnicity
- Disability
- Religion or belief
- Sexual orientation
- Socioeconomic status (where relevant)
Bias Mitigation Actions
When bias is detected:
- Document the bias finding, including affected groups and severity
- Assess whether the bias causes real-world harm
- Mitigate through data rebalancing, model adjustment, or process change
- Retest to confirm mitigation is effective
- Escalate to the AI governance committee if bias persists in a high-risk system
Bias Incident Log
| Date | System | Bias Found | Severity | Action Taken | Status |
|---|---|---|---|---|---|
Transparency & Explainability
Transparency & Explainability
Disclosure Requirements
Customer-facing AI:
- Customers must be informed when they are interacting with an AI system (e.g. chatbot, automated email)
- Where AI makes or significantly influences a decision about a person (e.g. loan approval, insurance pricing), we will provide a clear explanation of the key factors
- Individuals have the right to request human review of automated decisions under GDPR Article 22
Employee-facing AI:
- Employees must be informed when AI tools are used in performance management, recruitment, or scheduling
- Training must be provided on how AI tools work and their limitations
Internal AI:
- All AI models must be documented in the AI inventory with purpose, data sources, and owner
- Model cards or similar documentation must be maintained for all Tier 2 and Tier 3 systems
Explainability Standards by Risk Tier
| Risk Tier | Explainability Requirement |
|---|---|
| Tier 1 (Low) | General documentation of purpose and function |
| Tier 2 (Medium) | Feature importance or key factor explanation available |
| Tier 3 (High) | Individual-level explanations available on request; human review option |
AI Inventory
Maintain a register of all AI systems:
| System Name | Purpose | Risk Tier | Data Types | Owner | Last Reviewed |
|---|---|---|---|---|---|
Instructions
How to use this template
Review and customise the principles
Adapt the six core principles to reflect your organisation's values, industry, and regulatory obligations.
Involve legal and compliance
Have your legal team review the policy for alignment with GDPR, the EU AI Act, and any industry-specific regulations.
Get executive endorsement
The policy should be endorsed by the CEO or board to signal organisational commitment. This is not just an IT document.
Train all employees
Run mandatory training sessions on the policy. Focus on practical scenarios: what tools can employees use, what data can they share with AI, and when to escalate concerns.
Implement monitoring and review
Set up processes to monitor compliance and review the policy annually or when significant regulatory changes occur.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
Not yet mandatory in most jurisdictions, but the EU AI Act, GDPR, and sector-specific regulations require many of the elements covered by this policy. Having a policy also demonstrates due diligence and can reduce liability.
The AI ethics policy complements your data protection policy. Data protection focuses on personal data rights and GDPR compliance; AI ethics covers broader concerns like fairness, bias, and transparency that go beyond data protection.
Publishing key principles builds trust with customers and stakeholders. You can publish a summary of your principles while keeping operational details internal.
Include the AI ethics policy in your code of conduct and link violations to existing disciplinary procedures. Focus first on education — most violations come from lack of awareness, not malice.
Include ethical requirements in vendor contracts and RFPs. If an existing vendor does not meet standards, work with them on a remediation plan with a clear deadline, or begin transition to an alternative.
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