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The AI Risk Management framework differs from other risk management frameworks in its specific focus on the risks associated with AI applications. It proposes a disciplined governance loop that begins with precise risk identification and organizes hazards through risk categorization. The framework uses a risk appetite diagonal to define maximum acceptable exposure before numbers bias the conversation. Once calibrated, the populated frame overlays enumerated risks which ones exceed appetite and which sit safely within the risk tolerance band. Additionally, separate tables list Respond, Monitor, and Accept actions to itemize the execution consequences that appetite decisions carry. This level of detail and specificity in managing AI risks sets it apart from other more general risk management frameworks.
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The initial frame of the canvas presents a risk appetite diagonal to define maximum acceptable exposure before numbers bias the conversation. Once calibrated, the populated frame overlays enumerated risks which ones exceed appetite and which sit safely within the risk tolerance band. Additionally, separate tables list "Respond", "Monitor", and "Accept" actions to itemize the execution consequences that appetite decisions carry.
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How to watch out for the pitfalls of AI applications as they scale to the enterprise level? Our AI Risk Management presentation proposes a disciplined...
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