Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance

• URL: http://arxiv.org/abs/2407.14543v1
• Date: Tue, 16 Jul 2024 07:56:29 GMT
• Title: Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance
• Authors: Michał Kozielski, Marek Sikora, Łukasz Wawrowski,
• Abstract summary: Rule-based models offer a human-understandable representation, i.e. they are interpretable. The generation of such explanations involves the approximation of a black box model by a rule-based model. It has not been investigated whether the rule-based model makes decisions in the same way as the black box model it approximates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rule-based models offer a human-understandable representation, i.e. they are interpretable. For this reason, they are used to explain the decisions of non-interpretable complex models, referred to as black box models. The generation of such explanations involves the approximation of a black box model by a rule-based model. To date, however, it has not been investigated whether the rule-based model makes decisions in the same way as the black box model it approximates. Decision making in the same way is understood in this work as the consistency of decisions and the consistency of the most important attributes used for decision making. This study proposes a novel approach ensuring that the rule-based surrogate model mimics the performance of the black box model. The proposed solution performs an explanation fusion involving rule generation and taking into account the feature importance determined by the selected XAI methods for the black box model being explained. The result of the method can be both global and local rule-based explanations. The quality of the proposed solution was verified by extensive analysis on 30 tabular benchmark datasets representing classification problems. Evaluation included comparison with the reference method and an illustrative case study. In addition, the paper discusses the possible pathways for the application of the rule-based approach in XAI and how rule-based explanations, including the proposed method, meet the user perspective and requirements for both content and presentation. The software created and a detailed report containing the full experimental results are available on the GitHub repository (https://github.com/ruleminer/FI-rules4XAI ).

Related papers

• CLIMAX: An exploration of Classifier-Based Contrastive Explanations [5.381004207943597]
  We propose a novel post-hoc model XAI technique that provides contrastive explanations justifying the classification of a black box. Our method, which we refer to as CLIMAX, is based on local classifiers. We show that we achieve better consistency as compared to baselines such as LIME, BayLIME, and SLIME.
  arXiv  Detail & Related papers  (2023-07-02T22:52:58Z)
• Learning with Explanation Constraints [91.23736536228485]
  We provide a learning theoretic framework to analyze how explanations can improve the learning of our models. We demonstrate the benefits of our approach over a large array of synthetic and real-world experiments.
  arXiv  Detail & Related papers  (2023-03-25T15:06:47Z)
• Studying How to Efficiently and Effectively Guide Models with Explanations [52.498055901649025]
  'Model guidance' is the idea of regularizing the models' explanations to ensure that they are "right for the right reasons" We conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets. Specifically, we guide the models via bounding box annotations, which are much cheaper to obtain than the commonly used segmentation masks.
  arXiv  Detail & Related papers  (2023-03-21T15:34:50Z)
• Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective [0.4749981032986242]
  Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged to explain black box models. Xirt is able to generate global explanations of tree-ensemble models and also local explanations of instances of models through IRT.
  arXiv  Detail & Related papers  (2022-10-18T15:30:14Z)
• CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations [0.0]
  This paper introduces CARE, a modular explanation framework that addresses the model- and user-level desiderata. As a model-agnostic approach, CARE generates multiple, diverse explanations for any black-box model.
  arXiv  Detail & Related papers  (2021-08-18T15:26:59Z)
• Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification [0.0]
  We propose an LTCN-based model for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. Our interpretable model obtains competitive performance when compared to the state-of-the-art white and black boxes.
  arXiv  Detail & Related papers  (2021-07-07T18:14:50Z)
• An exact counterfactual-example-based approach to tree-ensemble models interpretability [0.0]
  High-performance models do not exhibit the necessary transparency to make their decisions fully understandable. We could derive an exact geometrical characterisation of their decision regions under the form of a collection of multidimensional intervals. An adaptation to reasoning on regression problems is also envisaged.
  arXiv  Detail & Related papers  (2021-05-31T09:32:46Z)
• 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)
• Contrastive Explanations for Model Interpretability [77.92370750072831]
  We propose a methodology to produce contrastive explanations for classification models. Our method is based on projecting model representation to a latent space. Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
  arXiv  Detail & Related papers  (2021-03-02T00:36:45Z)
• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology [66.06153115971732]
  We present a method for quantifying disentanglement that only uses the generative model. We empirically evaluate several state-of-the-art models across multiple datasets.
  arXiv  Detail & Related papers  (2020-06-05T20:54:11Z)
• Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles [78.6363825307044]
  We propose Explainable Matrix (ExMatrix), a novel visualization method for Random Forest (RF) interpretability. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.
  arXiv  Detail & Related papers  (2020-05-08T21:03:48Z)

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.