Paper Number
2306
Paper Type
Complete Research Paper
Abstract
E-participation and urban participation platforms have gained significant popularity in addressing thousands of participants. Governments and urban developers utilize them to collect citizens' input to develop user-centric cities and services. Handling these inputs has become increasingly challenging as it requires manual analysis, which is time-consuming, inconsistent, and expensive. Therefore, we examine how to design IT artifacts to support the AI-based analysis of citizens' inputs from e-participation. To do so, we build upon existing literature reviews that highlight the possibilities of AI and apply the design science research paradigm by conducting expert interviews and developing an AI-based prototype, which we evaluated with a focus group. Our initial design theory specifies six design principles addressing the process and AI-based functions to enable the automatized analysis of citizens' inputs, supporting e-participation experts. In addition, we emphasize that further research addressing the explainability of ML, more accurate models, trust, and human-AI interaction is required.
Recommended Citation
Borchers, Marten; Cao, Thu-Bao; Tavanapour, Navid; and Bittner, Eva A. C., "Designing AI-Based Systems to Support the Analysis of Citizens' Inputs from E-Participation" (2024). ECIS 2024 Proceedings. 7.
https://aisel.aisnet.org/ecis2024/track23_designresearch/track23_designresearch/7
Designing AI-Based Systems to Support the Analysis of Citizens' Inputs from E-Participation
E-participation and urban participation platforms have gained significant popularity in addressing thousands of participants. Governments and urban developers utilize them to collect citizens' input to develop user-centric cities and services. Handling these inputs has become increasingly challenging as it requires manual analysis, which is time-consuming, inconsistent, and expensive. Therefore, we examine how to design IT artifacts to support the AI-based analysis of citizens' inputs from e-participation. To do so, we build upon existing literature reviews that highlight the possibilities of AI and apply the design science research paradigm by conducting expert interviews and developing an AI-based prototype, which we evaluated with a focus group. Our initial design theory specifies six design principles addressing the process and AI-based functions to enable the automatized analysis of citizens' inputs, supporting e-participation experts. In addition, we emphasize that further research addressing the explainability of ML, more accurate models, trust, and human-AI interaction is required.
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