@inproceedings{qian-etal-2026-thinking,
    title = "From ``Thinking'' to ``Justifying'': Aligning High-Stakes Explainability with Professional Communication Standards",
    author = "Qian, Chen  and
      Wang, Yimeng  and
      Chen, Yu  and
      Wu, Lingfei  and
      Stathopoulos, Andreas",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1232/",
    pages = "24628--24637",
    ISBN = "979-8-89176-395-1",
    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 {\textrightarrow} 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{\ensuremath{<}}0.001), and SEF achieves 83.9{\%} accuracy (+5.3 over CoT). These results suggest structured justification can improve verifiability and may also improve reliability. Code is available at https://github.com/cqian03/SEF."
}