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Short Abstract

This paper will present how Arter.dk, Denmark’s national biodiversity portal, integrates AI and machine learning to validate citizen-contributed species observations. We explore approaches to improve data credibility, including image recognition, data driven and expert-assisted validation.

Abstract

Citizen-generated biodiversity data holds immense promise for research, conservation and policy-making. However, its credibility depends on reliable validation processes that experts can trust. This paper explores how Arter.dk, Denmark’s national biodiversity portal, is integrating AI and machine learning to support the validation of species observations—while acknowledging the need for careful, transparent adaptation to expert workflows, including species experts in co-creation of new more automated validation processes.

While image recognition has become a common entry point for AI in biodiversity monitoring, we argue that AI must go beyond image classification to address challenges such as spatial anomalies and seasonal inconsistencies. At Arter.dk, we are experimenting with data driven and AI based concepts to detect outliers in location, time and more, at the time of observation as well as in the validation process.

The adoption of these tools is not purely technical—it is also cultural. Experts need to understand, trust, and gradually integrate AI and data driven outputs into their validation routines. We discuss how co-designing the workflow and allowing human-in-the-loop validation are essential steps toward building confidence in automated systems.

This paper contributes to the workshop’s goal of making citizen-generated data scientifically robust and policy-relevant. By sharing lessons from the Danish biodiversity portal, we invite discussion on how to harmonize validation standards across platforms.