MLCommons AILuminate v1.0 defines 12 hazard categories for AI safety benchmarking. ai-blackteam maps its harm categories to these hazard codes and includes the alignment in every report.

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Viewing the MLCommons mapping

This prints two tables:
  1. Hazard Taxonomy - all 12 MLCommons categories with descriptions
  2. Harm Category Alignment - how ai-blackteam’s categories map to MLCommons codes

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The 12 hazard categories

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ai-blackteam harm category alignment

ai-blackteam’s internal harm categories map to MLCommons hazard codes:

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Where it shows up

The MLCommons alignment appears in every ai-blackteam report:
  • HTML dashboard - a dedicated table at the bottom showing all harm-to-hazard mappings
  • Markdown report - an alignment table after the results
  • JSON report - an mlcommons_alignment object with code and name for each harm category

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Why MLCommons matters

MLCommons is an industry consortium (Google, Meta, Microsoft, NVIDIA, and others). Their AILuminate benchmark is becoming the standard way to measure AI safety across the industry. By mapping to their taxonomy, ai-blackteam results can be compared against other tools and vendors using the same hazard definitions. If you’re producing safety documentation for a model deployment, referencing MLCommons hazard codes gives your findings credibility with reviewers who know the benchmark.