Artificial intelligence for digital citizen participation: Design principles for a collective intelligence architecture

Abstract

The challenges posed by digital citizen participation and the amount of data generated by Digital Participation Platforms (DPPs) create an ideal context for the implementation of Artificial Intelligence (AI) solutions. However, current AI solutions in DPPs focus mainly on technical challenges, often neglecting their social impact and not fully exploiting AI's potential to empower citizens. The goal of this paper is thus to investigate how to design digital participation platforms that integrate technical AI solutions while considering the social context in which they are implemented. Using Collective Intelligence as kernel theory, and through a literature review and a focus group, we generate design principles for the development of a socio-technically aware AI architecture. These principles are then validated by experts from the field of AI and citizen participation. The principles suggest optimizing the alignment of AI solutions with project goals, ensuring their structured integration across multiple levels, enhancing transparency, monitoring AI-driven impacts, dynamically allocating AI actions, empowering users, and balancing cognitive disparities. These principles provide a theoretical basis for future AI-driven artifacts, and theories in digital citizen participation.