{
  "id": 7924116,
  "title": "Probabilistic Linear Explanations",
  "url": "https://urgent.news/2026/09/16/probabilistic-linear-explanations",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-16T17:18:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.19077v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human cognitive limits by involving too many features, while probabilistic relaxations have remained largely limited to categorical classification. We present a unified framework for probabilistic explainability based on sparse, anchored linear models,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}