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Explainability meets uncertainty quantification
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Orientador(es)
Resumo(s)
Feature importance evaluation is one of the prevalent approaches to interpreting Machine Learning (ML) models. A drawback of using these methods for high-dimensional datasets is that they often lead to high-dimensional explanation output that hinders human analysis. This is especially true for explaining multimodal ML models, where the problem's complexity is further exacerbated by the inclusion of multiple data modalities and an increase in the overall number of features. This work proposes a novel approach to lower the complexity of feature-based explanations. The proposed approach is based on uncertainty quantification techniques, allowing for a principled way of reducing the number of modalities required to explain the model's predictions. We evaluated our method in three multimodal datasets comprising physiological time series. Results show that the proposed method can reduce the complexity of the explanations while maintaining a high level of accuracy in the predictions. This study illustrates an innovative example of the intersection between the disciplines of uncertainty quantification and explainable artificial intelligence.
Descrição
Funding Information: This work was supported by European funds through the Recovery and Resilience Plan, project “Center for Responsible AI” , project number C645008882-00000055 . Publisher Copyright: © 2023 The Author(s)
Palavras-chave
Complexity Explainable AI Feature-based explanations Multimodal SHAP Uncertainty quantification Software Signal Processing Information Systems Hardware and Architecture