Will using artificial intelligence to review camera trap images reduce human connection to wildlife research?

Abstract Camera traps have been widely adopted in wildlife research and management; however, the manual review of the large volumes of images they produce is time‐consuming and prone to errors. Artificial Intelligence (AI) platforms are becoming increasingly popular for automated image processing, as these tools can significantly reduce the time needed for reviewing images. Given the need for high‐quality, rapidly accessible data for implementing conservation actions in a changing world, the use of AI holds great promise for conservation. Despite the potential of AI, we raise concerns regarding the loss of the human element in wildlife data review. We argue AI may miss unexpected discoveries in images and diminish the personal connection to wildlife and conservation landscapes which is fostered through manual image review. As human values are pivotal in soliciting investment in conservation, AI may pose a risk through the loss of human connection to ecological systems. Further, outsourcing image review to AI represents a loss of training opportunities for the next generation of scientists. Manual review of images also engages citizen scientists in scientific discoveries, fostering enthusiasm for conservation careers and community support for conservation actions. While acknowledging the benefits of AI in processing wildlife camera trap images, we call for meaningful conversation on how AI should be used in the advancement of wildlife research. Given the recent challenges our field has faced with the advent of large language models (e.g., ChatGPT) in scientific training and research production, we should proactively begin conversations. We should prepare for discussion on alternative means of maintaining human connection to wildlife research, and alternative training opportunities for students and citizen scientists.