AI will change EA practice – but are we ready for it? A call for discussion based on developments in collecting and processing biodiversity data

ABSTRACT

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.

KEYWORDS:

• Artificial intelligence
• environmental assessment
• biodiversity
• machine learning
• data collection
• species

Acknowledgments

The experiences and challenges identified in this letter are in part gained from a number of research projects, including the project “International trends in EIA and SEA research and practice 2.0” funded by the German Environmental Agency (UBA) (grant agreement number, FKZ 3721131010) and the DREAMS project funded by the Innovation Fund Denmark (grant agreement number, 0177- 00021B DREAMS).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. “Edge computing: a distributed computing paradigm that brings computation to the ‘edge’ of a network by processing and analyzing data in real-time on the same device that collects the data, rather than sending all data to a centralized location for processing.” (Kays et al.& Wikelski 2023, 2).

Additional information

Funding

The work was supported by the Innovationsfonden [0177- 00021B DREAMS]; Umweltbundesamt [FKZ 3721131010].