arXiv cs.AI· Liming Zhu, Qinghua Lu, Ming Ding·· 10 小时前AI 评分22
理解优先的 AI 工程：面向人类理解的目的、参考架构与架构模式
Understanding-First AI Engineering: Purposes, Reference Architecture, and Architectural Patterns for Human Understanding
AI 导读
研究者提出“理解优先的 AI 工程”，将特定目的人类理解作为贯穿需求、设计与运行的一等设计原则。该框架把理解目的归为反思性能动、有效行动、集体治理与认知价值四类，并用共享记录连接生产、验证、使用与学习探索的参考架构，另给出五种候选架构模式。两个假设用例的定性情景分析展示了这些模式如何引导调查、参与和决策，但仅说明设计后果与局限，未验证实际效果。
正文 · 原文
Abstract:AI systems can produce reliable results without developing human understanding. However, human understanding remains valuable for shaping subsequent AI-supported work, forming preferences, governing collective choices and satisfying curiosity, even when human participation is unnecessary for correcting errors. We propose understanding-first AI engineering, which treats purpose-specific human understanding as a first-class design principle guiding requirements, design and operation. The purposes of understanding are organised into four complementary rationales: reflective agency, effective action, collective governance and epistemic value. A reference architecture connects production, verification, use, and learning and exploration through shared records, supporting understanding through explanation and participation. Five candidate architectural patterns guide choices about parallel learning, explanatory models, goal exploration, audience-specific views and distributed expertise. Qualitative scenario analysis of two hypothetical use cases demonstrates how the purpose classification, reference architecture and selected patterns guide investigation, participation and decision processes while retaining operational controls. The cases illustrate design consequences and limitations rather than establish empirical effectiveness. The approach makes human understanding an explicit engineering commitment whose justification need not depend on improving the original AI task.
| Subjects: | Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2611.13022 [cs.AI] |
| (or arXiv:2611.13022v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13022 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qinghua Lu [view email]
[v1]
Mon, 9 Nov 2026 07:26:25 UTC (4,392 KB)
来源：arXiv cs.AI · arxiv.org