{
  "annotation_id": "a81c7436-e3d5-4074-99b2-05bccd471d77",
  "date_created": "2026-02-17T05:53:12.014000Z",
  "date_modified": "2026-02-17T05:53:12.014000Z",
  "file_hash": "36c6bd9c39026a56d3b54a7dc851214b2a053fadadadfc8d4f2e3afd94be44ce",
  "private": false,
  "record": {
    "abstract": "Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hallucinations can yield conclusions that do not reliably align with their rationale. Thus, we propose \"Result -\u003e Justify\", which constrains the output communication to present a conclusion before its structured justification. We introduce SEF (Structured Explainability Framework), operationalizing professional conventions (e.g., CREAC, BLUF) via six metrics for structure and grounding. Experiments across four tasks in three domains validate this approach: all six metrics correlate with correctness (r=0.20-0.42; p\u003c0.001), and SEF achieves 83.9% accuracy (+5.3 over CoT). These results suggest structured justification can improve verifiability and may also improve reliability.",
    "arxiv_id": "2601.07233",
    "authors": [
      "Chen Qian",
      "Yimeng Wang",
      "Yu Chen",
      "Lingfei Wu",
      "Andreas Stathopoulos"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "From \"Thinking\" to \"Justifying\": Aligning High-Stakes Explainability with Professional Communication Standards",
    "url": "https://arxiv.org/abs/2601.07233",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5e1579f0-b3b1-4160-8275-9827979d3d50",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}