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Updated: Mar 10, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Building trust with marginalized communities in participatory acoustic monitoring through dynamic consent
Joycelyn Longdon1, Emmanuel Acheampong2, Jennifer Gabrys3
1Computer Laboratory, University of Cambridge, Cambridge, UK.
Abstract:
There exists a growing suite of technologies that support significant and exciting progress in biodiversity conservation and research. Citizen scientist participation is common in this research and often focuses on data collection and labeling. Yet, ongoing challenges exist concerning trust in participatory monitoring projects engaging Indigenous Peoples or local communities. These challenges are rooted in the proliferation of Western-centric approaches to engagement and uneven power dynamics between researchers and participants. Using passive acoustic monitoring (PAM) as a model, we explored how researchers can build trust in participatory research with conservation technologies. Working closely with 12 members of a forest fringe community in Ghana, we conducted semistructured interviews investigating community members' perceptions of and concerns with ecoacoustic technologies and a series of participatory workshops exploring ecoacoustic data practices. Through our interviews, we found that 4 key themes-questioning, agency, proof, and knowledge-shaped community members' sense of trust when engaging with conservation and technology systems or practices. Our engagements highlighted a need for a dynamic consent process, which entails a set of engagements and activities tailored to community members' needs, to ensure they could make informed decisions on their involvement in research projects. To facilitate more ethical and just community engagements that result in higher quality data and more successful conservation outcomes, we recommend that researchers working with conservation technologies and marginalized communities respond to suspicion, address agency, center community knowledge, and demonstrate data practices.
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