The Susceptibility of Example-Based Explainability Methods to Class Outliers
This study explores the impact of class outliers on the effectiveness of example-based explainability methods for black-box machine learning models. We reformulate existing explainability evaluation metrics, such as correctness and relevance, specifically for example-based methods, and introduce a new metric, distinguishability. Using these metrics, we highlight the shortcomings of current example-based explainability methods, including those who attempt to suppress class outliers. We conduct experiments on two datasets, a text classification dataset and an image classification dataset, and evaluate the performance of four state-of-the-art explainability methods. Our findings underscore the need for robust techniques to tackle the challenges posed by class outliers.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage Classificationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Toward Scalable and Unified Example-based Explanation and Outlier Detection
When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features that have contributed to the decision. At …
Decision MakingOutlier DetectionExplainability-Based Adversarial Attack on Graphs Through Edge Perturbation
Despite the success of graph neural networks (GNNs) in various domains, they exhibit susceptibility to adversarial attacks. Understanding these vulnerabilities is crucial for developing robust and secure applications. In…
Adversarial AttackNode ClassificationMonitoring and explainability of models in production
The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and…
BIG-bench Machine LearningRobust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version
Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging re…
Outlier DetectionTime SeriesTime Series AnalysisGranular Ball Twin Support Vector Machine
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture ModelsTwin support vector machine (TSVM) is an emerging machine learning model with versatile applicability in classifica…
Computational Efficiency