Key points are not available for this paper at this time.
The opportunities and potential of advanced digitalisation involving the application of Artificial Intelligence (AI) in Environmental Assessment (EA) are often mentioned across international studies. However, it is essential for us in EA research and practice to comprehensively grasp the implications of this transformation and proactively prepare for the imminent changes. In this context and drawing on insights from biological sciences, this letter examines the established use, prospects and risks of these technological advances in the field of species, habitat and biodiversity related data and its analysis. We aim to initiate a thought-provoking dialogue across diverse groups of EA actors regarding the practical implications of AI for EA, highlighting new roles and evolving skills needed to guarantee quality and legal compliance. Central to this discussion is the origination of data, alongside the distribution of responsibilities across actors/stakeholders involved in EA with regard to data collection, sharing and interpretation. Key considerations regard the quality and integrity of AI-supported and systematically collected data and the prevention of potential manipulation. We emphasise the need to re-evaluate education and training programs, adapt practices, and enhance decision-making processes as initial steps toward establishing a focused research agenda.
Sandfort et al. (Fri,) studied this question.