Image recognition as a “Dialogic AI Partner” within biodiversity citizen science—an empirical investigation

We investigate the potential of a new citizen science paradigm that facilitates collaborative learning between humans and artificial intelligence (AI). Recognising the potential of AI to support and empower rather than replace human participation, we explore the integration of image recognition as a ‘dialogic AI partner’ in citizen science (CS) projects, interacting with participants in real time. We study this in the context of a biodiversity monitoring project that relies on volunteers to identify biological species from images taken in the wild. Guided by the idea of Bakhtin’s dialogism and Bayesian inference principles, we developed a web interface that integrated an image recognition model, fine-tuned for classifying 22 UK bumblebee species, into an interactive interface based on visual feature keys to enable real-time dialogue between humans and AI. We report a significant improvement in identification accuracy for both humans and AI when they engage in such dialogue and retain the ability to reach independent conclusions rather than achieve consensus. Given the inherent need for convergence in decision-making within scientific processes such as species identification tasks, we augmented the dialogic process with a Bayesian model that unifies potentially divergent human and AI perspectives post collaboration to achieve a more accurate consensus decision than that achieved by either AI or citizens. Our work provides new understandings around the design of a dialogic space for CS practice that effectively builds on the complementary strengths of human and AI visual recognition approaches.