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Redesigning Risk Assessments in the Age of AI: Emergence of the Opacity Risk Dimension
1OMDS Department, ESSCA School of Management, Angers, France.
Summary
Traditional risk matrices fail for artificial intelligence (AI). This paper introduces the AI Risk Tensor (ART), a 3D framework adding "Opacity" to "Impact" and "Probability" to better govern AI risks.
Area of Science:
- Artificial Intelligence
- Risk Management
- Organizational Behavior
Background:
- Traditional risk analysis tools, like the Impact x Probability matrix, are inadequate for complex and unpredictable AI systems.
- Modern AI introduces novel challenges that existing risk frameworks cannot address.
- A critical gap exists in AI risk governance and implementation.
Purpose of the Study:
- To address the limitations of current risk analysis methods for artificial intelligence.
- To introduce a novel framework for assessing AI-specific risks.
- To enhance AI risk governance and accountability.
Main Methods:
- Comparative analysis of three documented AI failures.
- Introduction of the AI Risk Tensor (ART) framework.
- Operationalization of "Opacity" as a third dimension using a structured scoring rubric.
Main Results:
- The AI Risk Tensor (ART) redefines risk as a vector in (Impact, Probability, Opacity) space.
- ART transforms static risk matrices into a dynamic, three-dimensional "Risk Space."
- The framework provides an operational bridge for AI risk governance.
Conclusions:
- The AI Risk Tensor (ART) offers a more robust approach to managing AI risks.
- ART enhances decision-making and accountability in AI implementation.
- This novel framework is crucial for closing the AI risk governance implementation gap.
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