Practical and Ethical Issues in Big Data and Machine Learning Forecasts of Zambian Community Forestry Engagement

Altres ajuts: Unidad de excelencia María de Maeztu CEX2024-001506-M ; 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.