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AI Risk Management: McKinsey & Company
AI risk management, according to McKinsey & Company, involves identifying and addressing potential negative impacts of AI technology. This includes ensuring AI systems are safe, reliable, and ethical. It involves strategies to mitigate risks such as bias, security breaches, and job displacement. Effective AI risk management is crucial for maximizing the benefits of AI while minimizing potential harms.
McKinsey & Company’s framework emphasises the integration of business-minded legal and risk-management teams with data science teams from the outset of the AI development process. This collaborative “tech trust team” approach ensures that AI models align with social norms and legal requirements, maximizing business value while mitigating potential harms.
Key Features
The framework emphasises a structured risk-prioritisation plan, focusing on at least six overarching types of AI risks:
- Privacy
- Security
- Fairness
- Transparency and explainability
- Safety and performance
- Third-party risks
