Accepted Poster
Poster Short Abstract
AI has potential to rapidly advance citizen science, but its limits and impact must be understood. This study explores how the citizen science community defines and applies responsible, ethical, and trustworthy AI, considering its opportunities, drawbacks, volunteer impact, and future advancement.
Poster Abstract
The use of AI techniques (machine learning, natural language processing, and computer vision) is increasing rapidly across citizen science. 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 citizen 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 talk presents results of a study into how those who work in citizen science consider these terms when it comes to the use of AI. What are the opportunities and drawbacks of using AI techniques? What does it mean to be responsible, ethical, and trustworthy? How does the use of AI affect volunteers, and how can we ensure they remain engaged and understand their vital role in research? And how does the participatory science community think the use of AI should be advanced?
Poster Session