超越界面：重新定义社会参与式AI系统的用户体验
Beyond the Interface: Redefining UX for Society-in-the-Loop AI Systems
AI总结：
本文提出一种新的用户体验评估框架，通过分析社会技术动态，重新定义AI系统中的用户体验为多层次结构，涵盖性能、流程和信任等指标。
摘要：
人工智能系统日益在决策关键环境中运行，其中概率性输出和人类在环（HITL）交互重塑用户参与。传统用户体验（UX）框架，为确定性系统设计，无法捕捉这些不断演变的社会技术动态。本文认为，在AI赋能的HITL系统中，用户体验必须超越前端可用性，涵盖后端性能、组织流程和决策结构。我们采用混合方法，结合对269个利益相关者见解的归纳社会建构分析，以及部署一个操作性的HITL视频异常检测系统。我们的发现表明，利益相关者通过多维主题经历AI：风险、治理和组织能力。实验结果进一步显示，检测行为和警报路由直接校准人类监督和工作负荷。基于这些结果，我们正式化了一个新的评估框架，围绕四个社会技术指标：准确性（FPR/FNR）、操作延迟（响应时间）、适应时间（部署负担）和信任（验证的自动化规模）。该框架将用户体验重新定义为一个多层次的结构，涵盖基础设施和治理，为评估嵌入复杂现实世界生态系统中的AI系统提供严谨的基础。
英文摘要：
Artificial intelligence systems increasingly operate in decision-critical environments where probabilistic outputs and Human-in-the-Loop (HITL) interactions reshape user engagement. Traditional user experience (UX) frameworks, designed for deterministic systems, fail to capture these evolving sociotechnical dynamics. This paper argues that in AI-enabled HITL systems, UX must transcend frontend usability to encompass backend performance, organizational workflows, and decision making structures. We employ a mixed-methods approach, combining an inductive social construction analysis of 269 stakeholder insights with the deployment of an operational HITL video anomaly detection system. Our findings reveal that stakeholders experience AI through multifaceted themes: risk, governance, and organizational capacity. Experimental results further demonstrate how detection behavior and alert routing directly calibrate human oversight and workload. Grounded in these results, we formalize a new evaluative framework centered on four sociotechnical metrics: Accuracy (FPR/FNR), Operational Latency (response time), Adaptation Time (deployment burden), and Trust (validated automation scales). This framework redefines UX as a multi-layered construct spanning infrastructure and governance, providing a rigorous foundation for evaluating AI systems embedded within complex real-world ecosystems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
