KEYNOTE. THE DIGITAL DATA DIVIDE: USING SPECULATIVE FICTION TO FACILITATEPUBLIC DIALOGUE ON THE INCREASED USE OF PERSONAL DATA
In today's interconnected world, personal data generation and collection are growing exponentially. Increasingly, data from multiple sources, ranging from vital signs to environmental and lifestyle information, are combined to create detailed personal profiles (Brunning et al., 2023). While this enables more personalized care, it also raises questions about privacy, data reliability, ownership, and the desirability of collecting such data. To foster dialogue on these issues with a broad audience in an engaging and accessible way, the festival experience The Digital Data Divide (www.thedigitaldatadivide.nl) was developed (Groeneveld et al., 2024). This interactive experience draws on the concept of narrative learning, an approach that uses stories and dialogue to help individuals interpret and understand the world around them (Elsden et al., 2017). When applied as speculative fiction, narrative learning is especially effective for exploring potential future scenarios. By embedding these scenarios within a story, it encourages participants to critically examine and question their present viewpoints (Haste and Abrahams, 2008). The Digital Data Divide premiered at the Zwarte Cross Festival in the Netherlands in 2023 and has since been featured at various festivals, events, congresses, as well as in educational and healthcare settings and won the E³UDRES² Innovative Science Outreach Award in May 2025.
Authors
- Mirre den Ouden (ORCID: https://orcid.org/0000-0001-6980-9236)
- Rudolf M. Verdaasdonk (ORCID: https://orcid.org/0000-0002-3115-637X)
- Harmieke van Os‐Medendorp (ORCID: https://orcid.org/0000-0003-3300-2101)
- JEWC van Gemert-Pijnen
- Sjors Groeneveld (ORCID: https://orcid.org/0000-0003-3942-9691)
Institutions
- Saxion (NL)
- Inholland University of Applied Sciences (NL)
- University of Twente (NL)
Publication Details
- Journal
- SOCIETY TECHNOLOGY SOLUTIONS Proceedings of the International Scientific Conference
- Published
- 2025-11-04
- DOI
- https://doi.org/10.35363/via.sts.2025.109
- Primary Topic
- Data Analysis and Archiving
- Type
- article
- Field-Weighted Citation Impact
- 0.00