Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

XAI is essential for conservation decisions: The paper argues that explainable AI should become a standard component of ecological model validation because conservation practitioners need to understand not just whether a model is accurate, but why it is accurate—ensuring predictions are based on ecologically meaningful signals rather than spurious correlations or sampling biases that could undermine conservation efforts.

AI models can be fooled by irrelevant patterns: The case studies demonstrate that computer vision models may make correct predictions for the wrong reasons, such as relying on background features or shape confounds rather than actual animal characteristics. XAI methods revealed these false positives, showing why model transparency is critical before deploying AI in real conservation work.

Explainability improves model refinement: By using XAI techniques to audit their models, researchers identified specific problems like edge effects and occlusion issues in their harbor seal and cetacean detection systems. These insights directly guided decisions about data collection, augmentation, and retraining—making the models more reliable for actual field use.

XAI enables scaling conservation monitoring: While AI can dramatically expand the scale and speed of biodiversity monitoring across camera traps, drones, satellites, and underwater systems, explainability methods provide the transparency needed to confidently deploy these tools at scale without risking conservation decisions based on hidden model failures.