Classification
AI Risk Management
Overview
A Harms Matrix is a structured tool used to systematically assess and prioritize potential harms associated with AI systems by mapping the severity of harm (e.g., marginal, significant, critical) against the probability of occurrence (e.g., improbable, possible, probable). This matrix enables organizations to visualize and communicate risk exposure, supporting informed decision-making about mitigation strategies. The approach is particularly useful in complex environments where AI may have wide-ranging impacts, including unintended or emergent effects. One limitation of the Harms Matrix is that it relies on subjective estimation of probabilities and severities, which can introduce bias or error, especially in novel AI contexts where historical data is limited. Additionally, the matrix may oversimplify interdependent risks or fail to capture cumulative or systemic harms, making it important to supplement this tool with expert judgment and iterative review.
Governance Context
The Harms Matrix is referenced in several AI risk management and governance frameworks, such as the NIST AI Risk Management Framework (RMF) and the OECD AI Principles. These frameworks emphasize the need for systematic risk identification and prioritization, often mandating organizations to document potential harms and their likelihood (e.g., NIST RMF Core Function: 'Map' and 'Measure'). Concrete obligations include: (1) conducting regular risk assessments using structured tools like a Harms Matrix, (2) documenting and reviewing risk prioritization and mitigation actions, and (3) implementing controls to address high-priority risks (e.g., ISO/IEC 23894:2023 on AI risk management). The Harms Matrix also supports compliance with sector-specific regulations, such as the EU AI Act, which requires risk classification and mitigation for high-risk AI systems.
Ethical & Societal Implications
The Harms Matrix supports ethical AI development by making explicit the trade-offs between the likelihood and severity of harms, promoting transparency and accountability. However, it can reflect and perpetuate biases if input data or judgments are flawed, potentially overlooking harms to marginalized groups or misclassifying systemic risks. Its use must be complemented by stakeholder engagement and periodic reassessment to ensure that evolving societal values and emerging risks are adequately considered. Overreliance on the matrix without qualitative input can mask complex or intersectional harms.
Key Takeaways
A Harms Matrix visually maps severity versus probability of AI-related harms.; It enables prioritization of risk mitigation efforts based on structured analysis.; Subjectivity and data gaps can limit the accuracy of harm and probability estimates.; Widely referenced in leading AI risk management and compliance frameworks.; Should be supplemented with expert input and regular updates for comprehensive risk management.; Supports compliance with regulations requiring documented risk assessments.; May not capture cumulative, systemic, or intersectional harms without further analysis.