构建人机协作的计算设计框架，提升短视频儿童安全检测的可靠性与可解释性。
AI for Computational Design Science: A Responsible Human-AI Framework and Case Study on Short-Form Video Safety Surveillance
- 提出五阶段人机协同框架，AI扩展设计搜索，研究者掌控关键判断
- 儿童安全检测模型F1达0.769，显著优于通用模型，且支持概念级解释
- 适合关注AI伦理、内容安全与可解释系统设计的研究者
人工智能正在重塑信息系统的构建方式及设计研究的开展模式。然而，现有文献对人工智能在问题定义、资源构建、设计探索、评估与知识抽象等环节深度参与的计算设计科学（CDS）缺乏指导。本文提出AI for Computational Design Science（AI4CDS）框架，包含五个阶段：人工智能拓展问题与设计探索，研究人员保留领域根基、合理性验证、可验证性及科学判断责任。协作通过渐进信任、可逆性、可审计性与差异化可复现性进行治理。以ChildRiskGuard为例，该可解释工具用于检测不适合儿童的短格式视频，记录了AI交互过程、被拒方案、修正与审计轨迹。案例将受众依赖的安全性与解释一致性转化为三大技术挑战：分离通用风险与儿童特定风险，表征不同发展-风险机制，将概念级解释融入预测计算。ChildRiskGuard取得0.769的F1分数，明显优于通用内容安全模型，且与强基准相当。核心贡献为可负责任地赋能人工智能的计算设计科学框架；ChildRiskGuard提供了过程与成果证据，证明在人工智能扩展下、研究者主导的设计能生成并评估新型计算设计知识。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is transforming not only what information systems researchers design, but also how design research is conducted. Yet existing literature offers limited guidance for computational design science (CDS) when AI actively participates in problem formulation, resource construction, design search, evaluation, and knowledge abstraction. We develop AI for Computational Design Science (AI4CDS), a five-phase methodological framework in which AI expands problem and design search while researchers retain responsibility for domain grounding, admissibility, verification, and scientific judgment. Collaboration is governed by graduated trust, reversibility, auditability, and differentiated reproducibility. We instantiate AI4CDS through ChildRiskGuard, an interpretable artifact for detecting short-form videos inappropriate for children, while documenting AI interactions, rejected alternatives, corrections, and audit trails. The case translates audience-dependent safety and explanation faithfulness into three technical challenges and develops an artifact that separates generic from child-specific risk, represents distinct developmental-risk mechanisms, and makes concept-level explanations part of the predictive computation. ChildRiskGuard achieves an F1 score of 0.769, substantially outperforming direct application of a general-purpose content-safety model while remaining competitive with strong benchmarks. The primary contribution is AI4CDS as a responsible framework for AI-enabled CDS; ChildRiskGuard provides process and artifact evidence of how AI-expanded, researcher-governed design can generate and evaluate novel computational design knowledge.
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