Designing systemic relational insights. A new approach to sense-making with communities and artificial intelligence

The problematic area of research insists on the ethical, epistemological, and methodological terrain of citizen science, participatory methods, and artificial intelligence. It is a hybrid approach to sensemaking with communities that benefits from an emerging set of open social innovation principles. Inspired by theories of posthumanism and pluriversality, the research engages in a critical dialogue with emerging trends in open science and the disruptive potential of artificial intelligence. It introduces the concept of Systemic Relational Insight (SRI) - in the form of both a framework and a "scientific device" - that interweaves community-driven sensemaking with algorithmic support. It serves as an operational tool that transcends conventional dichotomies of qualitative and quantitative research methods, enabling a nuanced understanding of complex socio-techno-natural systems that seeks to overcome the problems of classical participatory approaches. Leveraging the strengths of both citizen science and participatory design, the framework offers a new epistemological lens through which local communities can conduct an in-depth analysis of their own systemic challenges. Through a comprehensive comparative analysis of the convergences and divergences between citizen science and participatory design, and a discussion on systemic design, network science, and artificial intelligence methodologies, the research culminates in a set of meta-design principles that are then applied in the conceptualization of the framework. When tested in the context of real-world case studies, it demonstrates its effectiveness in facilitating participatory sense-making activities and generating vertical insights into complex problems, thereby advancing the discourse on responsible technology adoption and the ethics of AI deployment. The dissertation outlines limitations and potential use cases such as participatory sensemaking at urban and territorial scales, and integration into local digital twins. The research highlights its potential to promote a more inclusive, ethical and effective approach to design research and practice within complex multi-stakeholder scenarios in the era of AI systems.