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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07233 |
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| _version_ | 1866917195889508352 |
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| author | Qian, Chen Wang, Yimeng Chen, Yu Wu, Lingfei Stathopoulos, Andreas |
| author_facet | Qian, Chen Wang, Yimeng Chen, Yu Wu, Lingfei Stathopoulos, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07233 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards Qian, Chen Wang, Yimeng Chen, Yu Wu, Lingfei Stathopoulos, Andreas Artificial Intelligence 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. |
| title | From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.07233 |