Characterizing the contribution of dependent features in XAI methods

• URL: http://arxiv.org/abs/2304.01717v1
• Date: Tue, 4 Apr 2023 11:25:57 GMT
• Title: Characterizing the contribution of dependent features in XAI methods
• Authors: Ahmed Salih, Ilaria Boscolo Galazzo, Zahra Raisi-Estabragh, Steffen E. Petersen, Gloria Menegaz, Petia Radeva
• Abstract summary: We propose a 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.
• Score: 6.990173577370281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity [0.0]
  We propose an Additive Effects of Collinearity (AEC) as a novel XAI method to consider the collinearity issue. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method.
  arXiv  Detail & Related papers  (2024-10-30T07:00:30Z)
• Metric Tools for Sensitivity Analysis with Applications to Neural Networks [0.0]
  Explainable Artificial Intelligence (XAI) aims to provide interpretations for predictions made by Machine Learning models. In this paper, a theoretical framework is proposed to study sensitivities of ML models using metric techniques. A complete family of new quantitative metrics called $alpha$-curves is extracted.
  arXiv  Detail & Related papers  (2023-05-03T18:10:21Z)
• Explanation-by-Example Based on Item Response Theory [0.0]
  This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.
  arXiv  Detail & Related papers  (2022-10-04T14:36:33Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Tree-based local explanations of machine learning model predictions, AraucanaXAI [2.9660372210786563]
  A tradeoff between performance and intelligibility is often to be faced, especially in high-stakes applications like medicine. We propose a novel methodological approach for generating explanations of the predictions of a generic ML model.
  arXiv  Detail & Related papers  (2021-10-15T17:39:19Z)
• Instance-Based Neural Dependency Parsing [56.63500180843504]
  We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set.
  arXiv  Detail & Related papers  (2021-09-28T05:30:52Z)
• Hessian-based toolbox for reliable and interpretable machine learning in physics [58.720142291102135]
  We present a toolbox for interpretability and reliability, extrapolation of the model architecture. It provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an agnostic score for the model predictions. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
  arXiv  Detail & Related papers  (2021-08-04T16:32:59Z)
• Rational Shapley Values [0.0]
  Most popular tools for post-hoc explainable artificial intelligence (XAI) are either insensitive to context or difficult to summarize. I introduce emphrational Shapley values, a novel XAI method that synthesizes and extends these seemingly incompatible approaches. I leverage tools from decision theory and causal modeling to formalize and implement a pragmatic approach that resolves a number of known challenges in XAI.
  arXiv  Detail & Related papers  (2021-06-18T15:45:21Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.