AI Risk Management
Artificial Intelligence Management System
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AI-Specific Risks
AI systems introduce unique ethical, legal, and technical risks needing careful management.
Ethical
AI can perpetuate biases or make unfair decisions affecting individuals or groups.
Legal
Concerns arise around data privacy, intellectual property, and accountability for AI-generated harm.
Technical
Risks include model brittleness, adversarial attacks, and unpredictable "black box" behavior.
Managing AI Risks
Effective strategies help mitigate potential harm and ensure responsible AI deployment.
Risk Avoidance
Choose not to undertake activities that pose unacceptable AI risks.
Risk Reduction
Implement controls to minimize the likelihood or impact of AI risks.
Risk Sharing
Transfer some AI risk to another party, like through insurance.
Risk Acceptance
Consciously decide to bear certain AI risks after assessment.
Monitoring Controls
Regularly check the effectiveness of implemented AI risk controls.
Contingency Planning
Develop plans to respond if identified AI risks materialize.
AI Traceability Essentials
These tools provide transparency and accountability for AI system operations.
Audit Trails
Chronological records of events within an AI system for review.
Data Lineage
Tracks data's journey from origin to its use in AI models.
Decision Logs
Records specific inputs and outputs of AI decision-making.
Model Versioning
Manages different iterations of AI models, ensuring traceability.
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