Introduction
While individual responsibility is essential, organisations must establish proper frameworks and practices to govern the use of AI technologies across the enterprise. Effective AI governance at the organisational level helps mitigate risks and ensures alignment with company values.
This document outlines practical principles for responsible AI adoption at the organisational level, focusing on actionable steps rather than abstract concepts. Remember that AI tools are designed to meet the objectives of the companies that create them—not necessarily your organisation's specific needs and values.
Practical Organisational Principles for Responsible AI
1. Know Who Owns Your AI
- Remember that cloud-based AI systems reflect the values and priorities of their creators
- Assess whether third-party AI aligns with your organisation's values and needs
- Consider developing in-house solutions for sensitive or mission-critical applications
- Create clear policies about which AI tools are approved for different types of work
- Document where AI is being used and maintain a register of approved systems
2. Implement Practical Transparency
- Require clear documentation of when and how AI is used in business processes
- Create standard disclaimers for AI-generated content
- Develop guidelines for disclosing AI use to customers and stakeholders
- Maintain accessible logs of AI system decisions for review
- Ensure employees can explain in simple terms how AI systems assist their work
3. Establish Clear Accountability Structures
- Designate specific roles for AI oversight (e.g., AI Ethics Officer)
- Create reporting lines for AI-related incidents or concerns
- Include AI management in performance reviews for relevant positions
- Develop protocols for addressing AI-related errors or issues
- Maintain insurance coverage for AI-related risks where appropriate
4. Build Fairness into Procurement and Development
- Require diversity impact assessments for AI tools before adoption
- Test AI systems on diverse datasets before implementation
- Regularly audit outputs for signs of bias or discrimination
- Create feedback mechanisms for users to report potential bias
- Include fairness metrics in AI performance evaluations
5. Implement Practical Risk Management
- Create a tiered system for AI use based on risk level
- Develop specific protocols for high-risk AI applications
- Run regular scenario planning for potential AI failures or misuse
- Maintain backup systems for AI-dependent processes
- Implement circuit breakers that can halt AI systems if they exceed parameters
6. Make Auditability a Requirement
- Only deploy AI systems that provide adequate audit trails
- Establish regular intervals for auditing AI systems
- Document the basis for significant AI-assisted decisions
- Maintain version control for AI models in use
- Create plain-language explanations of how AI systems make decisions
7. Ensure Meaningful Human Oversight
- Define clear human approval points for critical AI decisions
- Train staff in effective AI oversight techniques
- Rotate human reviewers to prevent automation bias
- Create metrics to evaluate the quality of human oversight
- Develop escalation procedures for challenging cases
8. Train and Support Your People
- Provide role-specific AI literacy training for all staff
- Create communities of practice for sharing AI experiences
- Develop clear career paths that incorporate AI skills
- Support ongoing education about AI developments
- Recognize and reward responsible AI innovation