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

Robin Sandfort, Birthe Uhlhorn, Gesa Geiβler, Ivar Lyhne, Alexandra Jiricka-Pürrer*

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
JournalImpact Assessment and Project Appraisal
Volume42
Issue number2
Pages (from-to)200-208
Number of pages9
ISSN1461-5517
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 IAIA.

Keywords

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

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