Responsible, Trustworthy, and ethical AI in citizen science

The use of AI techniques (such as machine learning, natural language processing, and computer vision) is increasing rapidly across the participatory sciences. Such techniques are used throughout the research process, such as cleaning data before presenting it to volunteers, using volunteers to contribute to the training of machine learning models, in the field data collection aided by computer vision, and creating collaborations between humans and AI to make tasks more engaging and of higher quality. Whilst this can improve the scientific process, it is important to understand the limitations of the use of these techniques, as well as consider the impact on how volunteers understand their role in participatory science. How practitioners communicate the use of AI to volunteers and continue to emphasize the importance of their contributions is also extremely important. To be beneficial to all in the participatory science community, the use of AI must be responsible, ethical, and trustworthy. However, it is not always clear what this means and how it can be applied. This study asks: -What are the opportunities and drawbacks of using AI techniques in citizen science? -What does it mean to be responsible, ethical, and trustworthy in the use of AI? -How should the use of AI in citizen science be advanced? If you are (or have been) involved in planning, carrying out, or taking part in citizen science projects, you are invited to take part in an online survey about your perceptions of the use of AI in citizen science. The survey should take around 20 minutes and you will be asked about -Your experience with citizen science projects -Your experience of AI -Opinions on how/why AI should/should not be used in citizen science -The effects of AI use on volunteers -What the terms ‘responsible’, ‘trustworthy’, and ‘ethical’ mean in the context of using AI in citizen science -How citizen science and human-AI collaboration should develop in the future The results will be presented at the Conference for Advancing Participatory Science in Portland, Oregon, at the end of May 2025.