Summary: AI Risk Assessment – Health Framework
In the evolving landscape of AI, traditional scoring systems like CVSS fall short, especially when addressing health impacts from AI-driven interactions. The AI Risk Assessment – Health framework proposes a robust method to evaluate potential risks posed by large language models (LLMs).
Key Features:
- Focus on Human Safety: Prioritizes user health over technicalities, making it relevant for regulators and medical professionals.
- Comprehensive Scoring: Evaluates risks across seven dimensions, including:
- Physical Safety Impact (PhSI)
- Mental Health Impact (MHI)
- Vulnerable Population Impact (VPI)
- Unintentional Triggerability (UT)
- Manipulation/Bonding Impact (MBI)
- Triggered Proactive Safeguards (TPS)
- Triggered Reactive Safeguards (TRS)
This framework ensures a reliable risk score that facilitates timely intervention, protecting our most vulnerable populations.
💡 Engage and Share! Let’s foster safer AI practices. How do you view AI risks? Comment below!
