# RAIBR03-BP01 Identify the likelihood of each potential harm
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 Establish a risk rating methodology that considers the likelihood of the event occurring. The risk likelihood indicates the probability of a harmful event occurring when the system is deployed for the use case. 

 **Level of risk exposed if this best practice is not established:** High 

## Implementation considerations
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1.  Create a standardized likelihood scale with clear definitions. For example, establish ranges from *almost certain* (95%\+ probability) to *highly unlikely* (less than 5% probability). Include specific frequency ranges for each category to maintain consistent evaluation. 

1.  Document likelihood assessments with supporting evidence. For example, consider a content moderation system where historical data shows harmful content detection failures occur in 15% of edge cases, placing this risk in the *unlikely* category. Include rationale for each assessment. 

## Resources
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 **Related documents:** 
+  [Learn how to assess the risk of AI systems](https://aws.amazon.com/blogs/machine-learning/learn-how-to-assess-risk-of-ai-systems/) 
+  [NIST Risk Management Framework](https://csrc.nist.gov/projects/risk-management/about-rmf) 
+  [Responsible AI in the generative era](https://www.amazon.science/blog/responsible-ai-in-the-generative-era) 
+  [ISO/IEC 42001:2023](https://www.iso.org/standard/42001) A.5.2 AI system impact assessment process