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Short abstract:

In spring 2024, Data4Food2030 partners will employ a foresight methodology to explore future DE4FS pathways. This presentation will present the results and critically address traditional data practices, data colonialism, and ethical implications in data economy for food systems.

Long abstract:

The findings of a methodology in foresight applied to the data economy in food systems (DE4FS) will be presented. In the spring of 2024, Data4Food2030 partners will undertake a participatory scenario development to create potential pathways on the future of the DE4FS. A critical discussion will then evaluate how to reshape traditional data practices, addressing issues of data colonialism and its ethical implications in the future of DE4FS.

This presentation will first explore challenges, focusing on the porosity, and overlapping dataregulatory layers in food systems, and the challenging the conceptualization of the DE4FS. Secondly, we will summarise the foresight methodology, consisting of stakeholder mapping to identify the processes employed in constructing plausible scenarios. Emphasis will be placed on highlighting major trends and building narratives taking into account technological, legal, social, environmental and economic aspects. A backcasting approach will be used to shape pathways to realize desired aspects and impacts of scenarios, while averting undesired ones.

The third aspect of this presentation involves a critical examination of these. As Europe engages in a twin transition of digitalization and the Green Deal, existential questions about the need and use of data ensue. Drawing from decolonial scholarship in data extraction, we question the premise of data collection also posited as data colonialism. Our approach challenges conventional data practices, reframing the utilitarian discourse on data, restoring wellbeing eroded by data, articulating worldviews to resist datafication, protecting social realms from over-datafication, and encouraging collective efforts to establish social relations beyond the scope of data.