Community-in-the-loop: Creating Artificial Process Intelligence for Co-production of City Service
Community-in-the-loop: Creating Artificial Process Intelligence for Co-production of City Service
复制标题
DOI：
10.1145/3555176
复制
发表时间：
2022-11
影响因子：
--
通讯作者：
Ye Wang;Srichakradhar Nagireddy;C. Thota;Duy H. Ho;Yugyung Lee
中科院分区：
文献类型：
--
作者：
Ye Wang;Srichakradhar Nagireddy;C. Thota;Duy H. Ho;Yugyung Lee
社区对社区问题有第一手的了解。本研究以社会技术决策理论为基础，提出“人工过程智能”的概念，以提升社会技术问题解决的效率。解决的技术挑战是引导面向内部和面向外部的311分类之间的通信。因此，深度学习模型在来自堪萨斯城的311系统的数据上进行了训练：（1）基于双向编码器表示的变压器（BERT）分类模型，可以预测面向内部的311服务类别和处理该问题的城市部门;（2）平衡潜在狄利克雷分配（LDA）和BERT聚类（BLBC）归纳总结居民投诉并将主题映射到内部311服务类别的模型;（3）可以预测响应和完成时间的回归时间序列模型。我们的案例研究表明，这些模型可以提供所需的信息，互惠沟通，城市服务规划和社区展望。未来的研究应该探索像聊天机器人这样的界面设计，并对人工智能辅助的311系统的接受和传播进行更多的研究。
Communities have first-hand knowledge about community issues. This study aims to improve the efficiency of social-technical problem-solving by proposing the concept of "artificial process intelligence," based on the theories of socio-technical decision-making. The technical challenges addressed were channeling the communication between the internal-facing and external-facing 311 categorizations. Accordingly, deep learning models were trained on data from Kansas City's 311 system: (1) Bidirectional Encoder Representations from Transformers (BERT) based classification models that can predict the internal-facing 311 service categories and the city departments that handle the issue; (2) the Balanced Latent Dirichlet Allocation (LDA) and BERT clustering (BLBC) model that inductively summarizes residents' complaints and maps the main themes to the internal-facing 311 service categories; (3) a regression time series model that can predict response and completion time. Our case study demonstrated that these models could provide the information needed for reciprocal communication, city service planning, and community envisioning. Future studies should explore interface design like a chatbot and conduct more research on the acceptance and diffusion of AI-assisted 311 systems.