AI and Collective Data Governance in Crowdsourced Citizen Science: A Normative Framework Approach

In this perspectives paper, the authors review key factors generating problematic oversight and participatory data governance gaps around AI/ML applications and the role of crowdsourced citizen science in addressing these gaps. Within this context, there are challenges in a broader AI ecosystem of power asymmetries lacking participative governance of data use by commercial providers and in unregulated commodification of citizen science data. Such challenges potentially can diminish the effectiveness of crowdsourced citizen science approaches in the participatory data governance of AI/ML applications. Notwithstanding, crowdsourced citizen science has a keystone role in its ethical grounding in communities, and this study proposes consideration of crowdsourced citizen science as contributors to public interest policy and alternative models of collective data governance operationalization. These models can potentially support crowdsourced citizen science as co-partners through multi-stakeholder processes, such as public-private partnerships, across the AI/ML application lifecycle. A normative framework for collective data governance coalesces perspectives on emerging models, such as data commons and data cooperatives, and re-positions the notion of the ‘collective’ moving away from a wholly data-centric approach. Future research is required to address specific collective data governance risks, such as re-centralizing control despite participatory paradigms. Although these risks might be mitigated in evolving collective data governance partnerships focused on public good, these are yet to be tested more rigorously and, thus far, partnerships may only be practicable if there is a willing alignment and collective demonstration of societal benefits in an equally rapidly emerging monopolistic and solutionism-directed AI/ML ecosystem.