The integration of Artificial Intelligence (AI) into Citizen Science (CS) is transforming how communities collect, analyze, and share data, offering opportunities for enhanced efficiency, accuracy, and scalability of CS projects. AI technologies such as natural language processing, anomaly detection systems, and predictive modeling are increasingly being used to address challenges like CS data validation, participant engagement, and large-scale analysis in CS projects. However, this integration also introduces significant risks and challenges, including ethical concerns related to transparency, accountability, and bias, as well as the potential demotivation of participants through automation of meaningful tasks. Furthermore, issues such as algorithmic opacity and data ownership can undermine trust in community-driven projects. This paper explores the dual impact of AI on CS. It emphasizes the need for a balanced approach where technological advancements do not overshadow the foundational principles of community participation, openness, and volunteer-driven efforts. Drawing from insights shared during a panel discussion with experts from diverse fields, this paper provides a roadmap for the responsible integration of AI into CS. Key considerations include developing standards and legal and ethical frameworks, promoting digital inclusivity, balancing technology with human capacity, and ensuring environmental sustainability.
- Maryam Lotfian
- Christophe Claramunt
- Jens Ingensand
