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[2602.14430] A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
Summary
This article presents a unified framework for evaluating the robustness of machine-learning interpretability, specifically in the context of hydrocarbon prospect risking, addressing the limitations of existing XAI methods like LIME and SHAP.
Why It Matters
As machine learning models become integral in high-stakes fields like geophysics, understanding their decision-making processes is crucial. This framework enhances trust in AI by providing a more reliable evaluation of model interpretability, which is essential for informed decision-making in resource exploration.
Key Takeaways
- The framework addresses limitations in existing XAI methods like LIME and SHAP.
- It proposes a method to quantify necessity and sufficiency in model explanations.
- Robustness evaluations can improve trust in AI decision-making processes.
- The framework is particularly relevant for high-dimensional structured data in geophysics.
- Understanding model interpretability is crucial for effective hydrocarbon prospect risking.
Computer Science > Machine Learning arXiv:2602.14430 (cs) [Submitted on 16 Feb 2026] Title:A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking Authors:Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar, Ghassan AlRegib View a PDF of the paper titled A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking, by Prithwijit Chowdhury and 2 other authors View PDF HTML (experimental) Abstract:In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based classifiers trained on tabular data have been recently used to make faster decisions on these prospects. The lack of transparency in the decision-making processes of such models has led to the emergence of explainable AI (XAI). LIME and SHAP are two such examples of these XAI methods which try to generate explanations of a particular decision by ranking the input features in terms of importance. However, explanations of the same scenario generated by these two different explanation strategies have shown to disagree or be different, particularly for complex data. This is because the definitions of "importance" and "relevance" differ for different explanation strategies. Thus, grounding these ranked features using theoretically backed causal ideas of necessity and sufficiency can prove to be a more reliable...