Generalized Integrated Gradients: A practical method for explaining diverse ensembles
We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety of functions that arise from practical applications of ML in domains like financial services. GIG is constructed to overcome limitations of Shapley (1953) and Aumann-Shapley (1974), and has desirable properties when compared to other approaches. We prove GIG is the only correct method, under a small set of reasonable axioms, for providing explanations for mixed-type models or games. We describe the implementation, and present results of experiments on several datasets and systems of models.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients
In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection. The integrated gradient is an attribution method which measures the contributions of input to the quantit…
Diabetic Retinopathy DetectionDelta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin critical decisions. While recent XAI methods …
Deep Integrated Explanations
This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coup…
Graph-based Integrated Gradients for Explaining Graph Neural Networks
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to gra…
Node ClassificationExplainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions difficult to interpret. Existing explainability methods rely on final-laye…
Document ClassificationHate Speech DetectionImage ClassificationSentiment Analysis