Characterizing the contribution of dependent features in XAI methods
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparent. In this context, many XAI methods were proposed being SHAP and LIME the most popular. However, the proposed methods assume that used predictors in the machine learning models are independent which in general is not necessarily true. Such assumption casts shadows on the robustness of the XAI outcomes such as the list of informative predictors. Here, we propose a simple, yet useful proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity.
Code (1)
Tasks
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Proportional infinite-width infinite-depth limit for deep linear neural networks
We study the distributional properties of linear neural networks with random parameters in the context of large networks, where the number of layers diverges in proportion to the number of neurons per layer. Prior works …
DescriptiveSensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming
Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs' inability to process signals and the attendant increased channel dim…
parameter estimationInvestigating writing style as a contributor to gender gaps in science and technology
A growing stream of research finds that scientific contributions are evaluated differently depending on the gender of the author. In this article, we consider whether gender differences in writing styles - how men and wo…
On kernel methods for covariates that are rankings
Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. T…
regressionLimitation of Characterizing Implicit Regularization by Data-independent Functions
In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself not completely defined and well understood…
Learning Theory