Practical and Ethical Issues in Big Data and Machine Learning Forecasts of Zambian Community Forestry Engagement
Pienkowski, Thomas (University of Kent. Durrell Institute of Conservation and Ecology)
Mills, Morena (Imperial College London. Centre for Environmental Policy)
Clark, Matt (Imperial College London. Centre for Environmental Policy)
Moombe, Kaala (The Center for International Forestry Research (Zambia))
Chilufya, Henry (Independent (Zambia))
Sfyridis, Alexandros (Imperial College London. Centre for Environmental Policy)
Sze, Jocelyne Shimin (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Olsson, Erik (Betty and Gordon Moore Center for Science. Conservation International (US))
Jørgensen, Andreas Christ Sølvsten (Imperial College London. Department of Mathematics)

Data:	2026
Resum:	Approaches integrating geospatial "big data" and machine learning will likely be increasingly used to predict conservation-related human behavior, such as patterns of local engagement, in socioecological systems. Yet, few studies evaluate both the technical and ethical aspects of such applications. Here, we provide a nation-scale worked example that combines machine learning and publicly available data to predict spatial patterns of Community Forestry establishment among 539,221 settlements across Zambia. Our model accurately predicted out-of-sample spatial establishment patterns three-quarters of the time (balanced accuracy = 76. 5%, sensitivity = 64. 0%, specificity = 89. 1%), though it had a high false positive rate (precision = 24. 3%). Accurately forecasting conservation establishment patterns for effective resource allocation requires better data on local preferences and programmatic decision-making, among other factors. Furthermore, such artificial intelligence applications risk making decision-making more technocratic, top-down, and opaque; therefore, they should only inform deliberation over possible future scenarios within wider, multistakeholder governance processes.
Ajuts:	European Commission 101054259 Agencia Estatal de Investigación CEX2024-001506-M
Nota:	Altres ajuts: Unidad de excelencia María de Maeztu CEX2024-001506-M
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Area-based conservation ; Artificial intelligence ; Community forestry ; Community-based conservation ; Forecasting ; Machine learning ; Predictive conservations science ; Scaling ; Socioecological systems
Publicat a:	Conservation letters, Vol. 19, Num. 2 (March-April 2026) , art. e70022, ISSN 1755-263X

DOI: 10.1111/con4.70022

12 p, 1.1 MB

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