{
  "id": 9243508,
  "title": "xWhyL: Causal Interactive Learning",
  "url": "https://urgent.news/2026/09/22/xwhyl-causal-interactive-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-22T11:40:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.26037v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite this, learning from those abductive signals is largely ignored in artificial intelligence. While explainable AI (XAI) increasingly draws on causal models to generate explanations, the converse direction about what…",
  "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."
}