arXiv:2411.08821v4 Announce Type: replace-cross Abstract: Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to individual observations. Current, popular methods, including LIME and SHAP, provide useful measures of feature contribution in the prediction space, while leaving opportunities for improved characterization of local structure in the model loss space. Additionally, they are not natively adapted for multi-class classification problems. We propose a new model-agno
The proliferation of AI models across critical applications necessitates more robust and interpretable decision-making processes, driving demand for advanced explainability techniques.
Improved local variable importance methods like CLIQE enhance trust and auditability in complex AI systems, crucial for deployment in regulated environments and high-stakes scenarios.
The ability to accurately characterize local model behavior in the loss space and adapt to multi-class problems offers more granular insights than previous state-of-the-art methods like LIME and SHAP.
- · AI ethicists
- · Machine learning researchers
- · Regulated industries deploying AI
- · Companies using multi-class classification models
- · Black-box AI models without explainability
- · LIME and SHAP as primary local importance methods
Increased adoption of explainable AI (XAI) practices and tools in enterprise and academic settings.
Faster development and deployment of reliable AI systems in domains requiring high transparency, such as healthcare and finance.
Potential for new regulatory frameworks to mandate specific levels of AI explainability, impacting model design and validation.
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