Artificial intelligence in environmental and Earth system sciences: explainability and trustworthiness

Abstract Explainable artificial intelligence (XAI) methods have recently emerged to gain insights into complex machine learning models. XAI can be promising for environmental and Earth system science because high-stakes decision-making for management and planning requires justification based on evidence and systems understanding. However, an overview of XAI applications and trust in AI in environmental and Earth system science is still missing. To close this gap, we reviewed 575 articles. XAI applications are popular in various domains, including ecology, engineering, geology, remote sensing, water resources, meteorology, atmospheric sciences, geochemistry, and geophysics. XAI applications focused primarily on understanding and predicting anthropogenic changes in geospatial patterns and impacts on human society and natural resources, especially biological species distributions, vegetation, air quality, transportation, and climate-water related topics, including risk and management. Among XAI methods, the SHAP and Shapley methods were the most popular (135 articles), followed by feature importance (27), partial dependence plots (22), LIME (21), and saliency maps (15). Alth