We explored the impact that recent developments in artificial intelligence (AI) might have on the future of citizen science. Rapid progress on large-language and multi-modal generative models in recent years is likely to impact all sectors of society. At the same time, we have also seen transformative applications of traditional supervised machine learning approaches within citizen science. 

• Citizen science apps can use AI algorithms to identify under-monitored geographic locations, incentivising participants to increase local data collection through competition.
• Data labelling can be used to generate large-scale training datasets to create models for science classification of images and sounds. 
• Autonomous robots and unmanned aerial vehicles can be equipped with smart sensors to enable wildlife surveillance in remote or difficult-to-access places, such as the automated monitoring of ocean buoys that can collect data on algal blooms.
• Newer deep learning techniques can enable rapid, accurate processing of animal footage by training algorithms to recognise species from labelled images. Citizen science can accelerate this by involving communities to capture and label images used to train deep learning models.

Examples of citizen science products harnessing AI include:

These are emerging developments at the intersection of AI and citizen science. We expect to see more of these types of applications in the coming years: 

This is a rapidly evolving field and AI will unquestionably impact the future of citizen science. Some early signals might become mainstream by 2030, while others might diminish. But AI could potentially shape many aspects of citizen science. It may even help us build better technology based on more diverse datasets. 

Check out our briefing paper on participatory AI to learn how to involve people in the design of AI tools.