Ethical AI Policy
Principles guiding responsible AI development and implementation.
Core Principles
Transparency
AI systems must be explainable and their decision-making processes documented for audit and review.
Fairness
Systems should avoid unfair bias, ensuring equitable outcomes for all users regardless of identity.
Accountability
Clear lines of responsibility must exist for AI development, deployment, and ongoing monitoring.
Privacy
User data must be protected at all stages, using encryption and strict access controls.
Governance Framework
Ethics Review Board
A multidisciplinary team that evaluates all AI initiatives for compliance with ethical standards before deployment.
Impact Assessments
Mandatory AI Impact Assessments (AIAs) required for all new projects involving decision-making systems.
Public Reporting
Annual publication of metrics measuring AI performance against ethical benchmarks and corrective actions taken.
Redress Mechanisms
Clear, accessible procedures for users to challenge or appeal decisions made by AI systems.
Technical Standards
Bias Mitigation
Use diverse training data sets and implement bias audits during and after model training.
Security by Design
Integrate security controls at every layer of the AI pipeline to prevent adversarial attacks.
Explainability Tools
Provide human-understandable explanations for critical decisions made by AI systems.
Who's Responsible
| Responsibility | Owner | Actions |
|---|---|---|
| Ethical AI design | Product Team | Conduct impact assessments, document trade-offs |
| Algorithm fairness | Data Science Team | Perform bias audits, test diverse data |
| Compliance monitoring | Legal & Compliance | Review audits, flag violations |