From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards

ACL ARR 2026 January Submission6684 Authors

Keywords: explainable AI, interpretability, structured prompting, chain-of-thought, justification, high-stakes NLP, evaluation metrics

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 → 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<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 and data will be released upon acceptance.

Paper Type: Short

Research Area: Special Theme (conference specific)

Research Area Keywords: explainability, prompting, evaluation and metrics, faithfulness, plausibility

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: English

Submission Number: 6684