{
  "id": "topic:model-interpretability",
  "_kind": "Topic",
  "_file": "domain/topics/topics-expanded.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Model Interpretability",
    "description": "Making ML model predictions understandable to humans — applying SHAP,\nLIME, attention visualization, feature importance analysis, and\ncounterfactual explanations to build trust, satisfy regulatory\nrequirements, and debug model behavior.\n"
  },
  "outgoingEdges": [
    {
      "from": "topic:model-interpretability",
      "to": "domain:ml-ai",
      "kind": "belongs_to_domain"
    },
    {
      "from": "topic:model-interpretability",
      "to": "domain:data-science",
      "kind": "belongs_to_domain"
    }
  ],
  "incomingEdges": []
}