ksb-d10-k0041

name: "KSB-D10-K0041" description: "AI Risk Classification Frameworks: Comprehensive understanding of AI risk classification frameworks applicable to pharmacovigilance inc..." version: "1.0.0" domain: "D10" domain_name: "Benefit-Risk Assessment" type: "Knowledge" proficiency_level: "L4" bloom_level: "evaluate" triggers: - "explain ai risk classification frameworks" - "what is ai risk classification frameworks" - "evaluate ai risk classification frameworks" epa_mapping: "EPA-03, EPA-05, EPA-08" cpa_mapping: "CPA-02, CPA-03" regulatory_refs: ""

KSB-D10-K0041: AI Risk Classification Frameworks

Overview

Domain: D10 - Benefit-Risk Assessment Type: Knowledge Proficiency Level: L4 (Proficient - Independent practice) Bloom Level: Evaluate

Description

Comprehensive understanding of AI risk classification frameworks applicable to pharmacovigilance including CIOMS WG XIV risk-based approach, EU AI Act high-risk classification criteria, FDA risk-based validation expectations, and organizational risk assessment methodologies. Knowledge of factors determining AI risk level: safety impact, autonomy level, reversibility of decisions, and affected population characteristics.

Context

• Major Section: AI Risk Assessment and Benefit-Risk Integration
• Section: Risk-based AI Implementation

EPA Mapping

• EPA-03:3006-3008
• EPA-05:3011-3012
• EPA-08:3018-3019

CPA Pathway

• CPA-02, CPA-03

Regulatory References

• General PV competency

Instructions

When this skill is activated, Claude should:

1. Demonstrate L4 proficiency in ai risk classification frameworks
2. Apply evaluate level cognitive skills to evaluate the topic
3. Reference relevant regulatory guidance (general PV standards)
4. Connect to related EPAs: EPA-03, EPA-05, EPA-08

Key Competencies

• Comprehensive understanding of AI risk classification frameworks applicable to pharmacovigilance including CIOMS WG XIV risk-based approach, EU AI Act high-risk classification criteria, FDA risk-based validation expectations, and organizational risk assessment methodologies. Knowledge of factors determining AI risk level: safety impact, autonomy level, reversibility of decisions, and affected population characteristics.

Assessment Criteria

• Can evaluate core concepts independently
• Demonstrates understanding of regulatory context
• Applies knowledge appropriately to PV scenarios

Related Skills

• Other D10 skills in Risk-based AI Implementation
• Cross-domain integrations per DAG architecture

Generated from PV KSB Framework v1.0 | 2025-12-31