The AI Safety Benchmark introduced by MLCommons aims to provide standards to assess the safety risks associated with AI systems. The new version 0.5 includes specific use cases and personas but it’s cautioned against using it for comprehensive safety assessment for now. The benchmark features a rigorous structure:

• Taxonomy of 13 hazard categories, with tests for 7 categories included.
• Principled approach in constructing and specifying the benchmark.
• Inclusion of various user personas to make the benchmark more comprehensive.
• Provides a grading system and an example evaluation for chat-tuned language models.

Opinion: This benchmark is an essential stepping stone towards the systematic evaluation of AI safety. Expanding this model could greatly enhance the reliability and security of AI applications in the future.