Our Standards
Comprehensive framework for responsible AI governance
AI Ethics Mark Standards v1.0
Our certification framework is built on five core pillars, encompassing 150+ specific assessment criteria. Organizations are evaluated across all dimensions to ensure comprehensive responsible AI practices.
1. Governance & Accountability
Organizations must establish clear ownership, oversight, and decision-making processes for AI systems.
Key Requirements:
- Designated AI governance committee or officer
- Documented AI policies and decision-making frameworks
- Regular governance reviews and updates
- Clear escalation procedures for AI-related issues
- Board-level oversight of AI strategy and risks
2. Transparency & Explainability
AI systems must be documented and their decision-making processes understandable to relevant stakeholders.
Key Requirements:
- Comprehensive AI system inventory and documentation
- Clear communication of AI capabilities and limitations
- Explainability mechanisms for high-impact decisions
- Disclosure when individuals interact with AI
- Public transparency reporting (Leadership level)
3. Fairness & Bias Mitigation
Organizations must actively identify, measure, and mitigate bias in AI systems.
Key Requirements:
- Bias assessment processes for AI systems
- Testing across relevant demographic and use case dimensions
- Documented mitigation strategies
- Regular fairness audits and monitoring
- Diverse teams involved in AI development
4. Privacy & Data Protection
AI systems must respect individual privacy and comply with data protection regulations.
Key Requirements:
- Compliance with GDPR, CCPA, and relevant regulations
- Data minimization and purpose limitation
- Privacy-preserving techniques where applicable
- Clear data retention and deletion policies
- Individual rights mechanisms (access, deletion)
5. Safety & Security
AI systems must be developed and deployed with appropriate safety and security measures.
Key Requirements:
- Risk assessment before AI system deployment
- Testing and validation procedures
- Continuous monitoring and incident response
- Security measures against adversarial attacks
- Human oversight for high-risk decisions
Standards Development
Our standards are developed through consultation with AI ethics experts, industry practitioners, and regulatory bodies. They are reviewed and updated annually to reflect evolving best practices and regulatory requirements.