Reframing biodiversity conservation using social data and artificial intelligence
Reframing biodiversity conservation using social data and artificial intelligence
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Original abstract
Human behaviour is central to biodiversity loss, yet conservation rarely harnesses the wealth of social datasets that capture and reflect these behaviours. Here, we argue that integrating diverse social data with recent advances in artificial intelligence (AI) can reveal early behavioural signals of emerging threats, enabling a shift from reactive mitigation to anticipatory action. Using illegal wildlife trade mitigation as an illustrative example, we outline how AI-enabled analyses of social data can be applied across the conservation project lifecycle, and highlight key safeguards and priority steps for piloting these approaches in policy-relevant settings.