Fraunhofer HHI experts underscore the value of XAI in Geosciences

Fraunhofer HHI experts underscore the value of XAI in Geosciences


    AI offers unparalleled opportunities for analyzing data and solving complex and nonlinear problems in geoscience. However, as the complexity of an AI model increases, its interpretability may decrease. In safety-critical situations, such as disasters, the lack of understanding of how a model works — and the resulting lack of trust in its results — can hinder its implementation.

    XAI methods address this challenge by providing insights into AI systems, identifying data- or model-related issues. For instance, XAI can detect ‘false’ correlations in training data — correlations irrelevant to the AI system’s specific task that may distort results.

    “Trust is crucial to the adoption of AI. XAI acts as a magnifying lens, enabling researchers, policymakers, and security specialists to analyze data through the ‘eyes’ of the model so that dominant prediction strategies — and any undesired behaviors — can be understood”, explains Prof. Wojciech Samek, Head of Artificial Intelligence at Fraunhofer HHI.

    The paper’s authors analyzed 2.3 million arXiv abstracts of geoscience-related articles published between 2007 and 2022. They found that only 6.1% of papers referenced XAI. Considering its immense potential, the authors sought to identify challenges preventing geoscientists from adopting XAI methods.

    Focusing on natural hazards, the authors examined use cases curated by the International Telecommunication Union/World Meteorological Organization/UN Environment Focus Group on AI for Natural Disaster Management. After surveying researchers involved in these use cases, the authors identified key motivations and hurdles.

    Motivations included building trust in AI applications, gaining insights from data, and improving AI systems’ efficiency. Most participants also used XAI to analyze their models’ underlying processes. Conversely, those not using XAI cited the effort, time, and resources required as barriers.

    “XAI has a clear added value for the geosciences — improving underlying datasets and AI models, identifying physical relationships that are captured by data, and building trust among end users — I hope that once geoscientists understand this value, it will become part of their AI pipeline”, says Dr. Monique Kuglitsch, Innovation Manager at Fraunhofer HHI and Chair of the Global Initiative on Resilience to Natural Hazards Through AI Solutions.

To support XAI adoption in geoscience, the paper provides four actionable recommendations:

1. Fostering demand from stakeholders and end users for explainable models.

2. Building educational resources for XAI users, covering how different methods function, explanations they can provide, and their limitations.

3. Building international partnerships to bring together geoscience and AI experts and promote knowledge sharing.

4. Supporting integration with streamlined workflows for standardization and interoperability of AI in natural hazards and other geoscience domains.

Explainable AI (XAI)
Geosciences
Interpretable Machine Learning
Data Transparency
Earth Observation
Remote Sensing
Climate Modeling
Environmental Monitoring
Predictive Analytics
Geological Data Analysis
Natural Hazard Prediction
Geospatial Analysis
Satellite Imagery
Model Interpretability
Deep Learning
Neural Networks
Data Visualization
Decision Support Systems

#FraunhoferHHI
#XAI
#ExplainableAI
#Geoscience
#AIinGeoscience
#MachineLearning
#DataScience
#RemoteSensing
#EarthObservation
#ArtificialIntelligence
#BigData
#AIResearch
#ClimateTech
#SustainableTech
#DeepLearning
#ScienceInnovation

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