CIENS Artifical Intelligence Network (CAIN)
Artificial Intelligence (AI) and machine learning have become ubiquitous in research, society, and industry alike. The role in addressing major global challenges and shaping future practices is increasingly recognised. CAIN, facilitated by CIENS partner institutions, supports the practical and critical integration of AI and machine learning in environmental and social science research.
Our purpose:
CAIN fosters a peer network for researchers interested in the use and implications of AI and machine learning across disciplines. Our primary goal is to support CIENS researchers by providing a platform for exchanging ideas, experiences, tools, and critical perspectives related to AI in research.
The network aims to strengthen both methodological competence and ethical awareness, helping researchers navigate the opportunities and risks associated with AI-assisted research practices.
What we do:
We organise seminars, workshops, webinars, and networking activities that encourage peer learning and open discussion on the ethical, responsible, and transparent use of AI in research. Our activities bring together researchers and invited experts to discuss practical applications, methodological developments, and emerging ethical challenges in AI and machine learning.
Topics include, among others, generative AI in scientific work, AI research ethics, data annotation, reproducibility, environmental impacts of AI, and the use of AI tools for coding, analysis, and knowledge synthesis.
The network is represented by Yuri Kasahara (NIBR), Taheera Ahmed (NINA), and Maximilian Nawrath (NIVA).
Interested in joining?
We welcome anyone working at a CIENS institute with an interest in AI in research, including both practical applications and critical reflection on its broader implications.
Sign up for our mailing list here to stay informed about upcoming workshops, seminars, and events.