A Typology for AI-enhanced Online Ideation: Application to Digital Participation Platforms

Abstract

Digital Participation Platforms (DPP) can enable a massive online participation of citizens in policy-making. However, this digital channel brings new challenges for citizens in the form of information overload and asynchronous dialogues. Various disciplines of online ideation provide different AI-based approaches to tackle these challenges, but the literature remains fragmented. In consequence, this paper develops a typology for online ideation consisting of six types of AI-enhanced solutions. The application of this typology to DPP shows a prominence of automated tasks, with few AI-human loop approaches, and a current lack of applications at the collective level. This general typology also allows us to compare current DPP solutions to other fields, such as open innovation or recommender systems, and to use these fields as inspiration for future solutions. Overall, this paper suggests a theoretical foundation to analyze AI-enhanced online ideation under the form of a typology. Its application to DPP enables identifying future research opportunities and serves as a basis to develop complex architectures for the use of AI in DPP.