Risk Management in AI Leadership:
1. Identifying Potential Risks
Conduct thorough risk assessments
Address technical challenges and ethical concerns
2. Mitigating Major Issues
Develop strategies for data quality and ethical guidelines
Implement regular audits and contingency plans
3. Ensuring Quality Assurance
Ensure accurate, reliable, and relevant data
Rigorous testing, validation, and regular updates
Empowering Teams and Delegation:
Delegate authority and responsibility
Provide necessary resources and foster collaboration
Workshops and Training:
Conduct structured workshops and training sessions
