FRAMEWORK

Five Principles of Trustworthy AI

Core principles that guide the development and deployment of AI systems that earn and maintain user trust.

Transparency

Users understand how AI systems work, what data is used, and how decisions are made.

Key Practices

  • •Clear disclosure of AI use
  • •Explainable decision-making processes
  • •Accessible documentation
  • •Open communication about limitations

Fairness

AI systems treat all users equitably and avoid discriminatory outcomes.

Key Practices

  • •Bias detection and mitigation
  • •Representative training data
  • •Regular fairness audits
  • •Inclusive design practices

Accountability

Clear responsibility for AI system outcomes and mechanisms for redress.

Key Practices

  • •Defined ownership and governance
  • •Audit trails and logging
  • •Complaint and appeal processes
  • •Regular impact assessments

Privacy

User data is protected, and privacy rights are respected throughout the AI lifecycle.

Key Practices

  • •Data minimization
  • •Strong security measures
  • •User control over personal data
  • •Privacy-preserving techniques

Safety

AI systems are reliable, secure, and designed to prevent harm.

Key Practices

  • •Robust testing and validation
  • •Risk assessment and mitigation
  • •Fail-safe mechanisms
  • •Continuous monitoring

Integrated Approach

These principles work together as a holistic framework. Implementing one without the others creates gaps in trust.

For example, transparency without privacy protection can expose sensitive information. Fairness without accountability lacks enforcement mechanisms. Safety without transparency prevents users from making informed decisions.

Our framework helps you implement all five principles in a coordinated way that builds genuine trust.