{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/mechanistic-interpretability",
  "headline": "Mechanistic Interpretability: Reverse-Engineering Neural Network Circuits and Features",
  "description": "Mechanistic interpretability treats neural networks as scientific objects to be reverse-engineered — locating the circuits, features, and computational pathways that produce specific behaviors. Instead of asking \"what does the model output?\", it asks \"how does the model compute this output?\" — enabling targeted fixes for safety, bias, and reliability.",
  "dateCreated": "2026-05-24T02:49:13.633Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Mechanistic Interpretability for AI Safety — A Review",
      "sameAs": "https://arxiv.org/abs/2404.14082"
    },
    {
      "@type": "CreativeWork",
      "name": "Bridging the Black Box: A Survey on Mechanistic Interpretability in AI",
      "sameAs": "https://dl.acm.org/doi/10.1145/3787104"
    }
  ]
}