Artificial intelligence (AI) is revolutionizing many fields, including geoscience, by providing new insights and tools for analyzing complex data. However, the use of AI in geoscience also raises important questions about the transparency and interpretability of these models. In particular, it’s important to understand how explainable AI (XAI) methods can be used to help users better understand and trust the decisions made by AI models.
Recently, convolutional neural networks (CNNs) have gained much recognition in the geoscience community due to their proficiency in recognizing nonlinear system behavior and uncovering predictive spatiotemporal patterns.
The growing interest in explainable artificial intelligence (XAI) is largely due to the need to understand how decisions are made by a convolutional neural network, given its black-box nature and the significance of prediction explanation.
A comparison is made between several of the most frequent XAI approaches, and their precision in understanding CNN decisions for geoscience objectives is analyzed.
We aim to make people aware of these techniques’ theoretical boundaries and to understand their respective strengths and weaknesses to determine the most suitable approach.
XAI methods are initially evaluated on an idealized attribution benchmark where the explanation of the network is known in advance, so their performance can be objectively measured.
Next, XAI is used in climate predictions to explain the CNN formulated to estimate the number of atmospheric rivers in daily climate simulations.
Our findings emphasize various key problems of XAI approaches (e.g., gradient shattering, incapability to recognize the sign of attribution, and unawareness of zero input) that have not been noticed before in our field. If these issues are not considered with caution, it can result in a skewed idea of the CNN decision-making procedure.
We expect our analysis to spur further exploration into XAI accuracy and aid in the careful implementation of XAI in geoscience, potentially enabling the use of CNNs and deep learning for anticipation issues to be extended.
Explainable artificial intelligence (XAI) methods for applications of convolutional neural networks (CNNs) in geoscience is an important and rapidly evolving field. As AI models become more complex and are applied to increasingly complex data, it’s crucial to develop and implement XAI methods that can provide insights into the decision-making processes of these models.
The fidelity of XAI methods for CNNs in geoscience depends on several factors, including the availability and quality of data, the model’s complexity, and the users’ ability to interpret and understand the results. While there are many challenges to overcome, recent advancements in XAI methods provide new tools and techniques for addressing these issues.