面向计算设计科学的人工智能：负责任的人机协同框架及短视频安全监控案例研究

中文摘要 AI 辅助

人工智能（AI）不仅正在改变信息系统研究者设计的内容，也在改变设计研究的开展方式。然而现有文献对计算设计科学（CDS）的指导有限，尤其是当AI积极参与问题构建、资源构建、设计搜索、评估和知识抽象等环节时。我们开发了面向计算设计科学的人工智能（AI4CDS），这是一个五阶段方法论框架，其中AI拓展问题与设计搜索，而研究者保留领域基础、可接受性、验证及科学判断的责任。协作受分级信任、可逆性、可审计性和差异化可复现性的约束。我们通过ChildRiskGuard实例化AI4CDS，这是一个用于检测不适宜儿童观看的短视频的可解释工具，同时记录AI交互、被拒绝的替代方案、修正内容及审计轨迹。该案例将依赖受众的安全性与解释忠实性转化为三个技术挑战，并开发出一种工具，该工具将通用风险与儿童特定风险分离、呈现不同的发展风险机制，并将概念级解释纳入预测计算。ChildRiskGuard的F1分数为0.769，显著优于通用内容安全模型的直接应用，同时与强基准模型具有竞争力。主要贡献是作为支持AI的负责任框架的AI4CDS；ChildRiskGuard提供了过程与工具层面的证据，证明AI拓展且由研究者管控的设计如何生成与评估新颖的计算设计知识。

英文摘要

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.

发表机构

• Iowa State University（爱荷华州立大学）
• University of Delaware（特拉华大学）
• City University of Hong Kong（香港城市大学）
• University of Arizona（亚利桑那大学）

机构由 AI 辅助整理，请以论文原文为准。