Minimizing False-Positive Attributions in Explanations of Non-Linear Models

• URL: http://arxiv.org/abs/2505.11210v3
• Date: Fri, 24 Oct 2025 19:49:15 GMT
• Title: Minimizing False-Positive Attributions in Explanations of Non-Linear Models
• Authors: Anders Gjølbye, Stefan Haufe, Lars Kai Hansen,
• Abstract summary: Suppressor variables can influence model predictions without being dependent on the target outcome.<n>These variables may cause false-positive feature attributions, undermining the utility of explanations.<n>We introduce PatternLocal, a novel XAI technique that addresses this gap.
• Score: 5.186535458271726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Suppressor variables can influence model predictions without being dependent on the target outcome, and they pose a significant challenge for Explainable AI (XAI) methods. These variables may cause false-positive feature attributions, undermining the utility of explanations. Although effective remedies exist for linear models, their extension to non-linear models and instance-based explanations has remained limited. We introduce PatternLocal, a novel XAI technique that addresses this gap. PatternLocal begins with a locally linear surrogate, e.g., LIME, KernelSHAP, or gradient-based methods, and transforms the resulting discriminative model weights into a generative representation, thereby suppressing the influence of suppressor variables while preserving local fidelity. In extensive hyperparameter optimization on the XAI-TRIS benchmark, PatternLocal consistently outperformed other XAI methods and reduced false-positive attributions when explaining non-linear tasks, thereby enabling more reliable and actionable insights. We further evaluate PatternLocal on an EEG motor imagery dataset, demonstrating physiologically plausible explanations.

Related papers

• Improving Local Fidelity Through Sampling and Modeling Nonlinearity [3.7080015862513847]
  Local Interpretable Model-agnostic Explanation (LIME) assumes that the local decision boundary is linear and fails to capture the non-linear relationships.<n>We propose a novel method that can generate high-fidelity explanations.
  arXiv  Detail & Related papers  (2025-12-05T09:26:18Z)
• Did Models Sufficient Learn? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation [61.248535801314375]
  Subset-Selected Counterfactual Augmentation (SS-CA)<n>We develop Counterfactual LIMA to identify minimal spatial region sets whose removal can selectively alter model predictions.<n>Experiments show that SS-CA improves generalization on in-distribution (ID) test data and achieves superior performance on out-of-distribution (OOD) benchmarks.
  arXiv  Detail & Related papers  (2025-11-15T08:39:22Z)
• Bayesian Model Parameter Learning in Linear Inverse Problems: Application in EEG Focal Source Imaging [49.1574468325115]
  Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly.<n>We studied a linear inverse problem that included an unknown non-linear model parameter.<n>We utilized a Bayesian model-based learning approach that allowed signal recovery and subsequently estimation of the model parameter.
  arXiv  Detail & Related papers  (2025-01-07T18:14:24Z)
• Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI [59.96044730204345]
  We introduce Derivative-Free Diffusion Manifold-Constrainted Gradients (FreeMCG) FreeMCG serves as an improved basis for explainability of a given neural network. We show that our method yields state-of-the-art results while preserving the essential properties expected of XAI tools.
  arXiv  Detail & Related papers  (2024-11-22T11:15:14Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling.<n>Yet their widespread adoption poses challenges regarding data attribution and interpretability.<n>We develop an influence functions framework to address these challenges.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• MASALA: Model-Agnostic Surrogate Explanations by Locality Adaptation [3.587367153279351]
  Existing local Explainable AI (XAI) methods select a region of the input space in the vicinity of a given input instance, for which they approximate the behaviour of a model using a simpler and more interpretable surrogate model. We propose a novel method, MASALA, for generating explanations, which automatically determines the appropriate local region of impactful model behaviour for each individual instance being explained.
  arXiv  Detail & Related papers  (2024-08-19T15:26:45Z)
• Robustness of Explainable Artificial Intelligence in Industrial Process Modelling [43.388607981317016]
  We evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. We show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.
  arXiv  Detail & Related papers  (2024-07-12T09:46:26Z)
• Scaling and renormalization in high-dimensional regression [72.59731158970894]
  We present a unifying perspective on recent results on ridge regression.<n>We use the basic tools of random matrix theory and free probability, aimed at readers with backgrounds in physics and deep learning.<n>Our results extend and provide a unifying perspective on earlier models of scaling laws.
  arXiv  Detail & Related papers  (2024-05-01T15:59:00Z)
• Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification [2.6699011287124366]
  Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass.
  arXiv  Detail & Related papers  (2024-02-06T18:09:05Z)
• A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints [87.08677547257733]
  Neuro-symbolic AI bridges the gap between purely symbolic and neural approaches to learning. We show how to maximize the likelihood of a symbolic constraint w.r.t the neural network's output distribution. We also evaluate our approach on Sudoku and shortest-path prediction cast as autoregressive generation.
  arXiv  Detail & Related papers  (2023-12-06T20:58:07Z)
• Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections [5.524804393257921]
  We show how to use eXplainable AI (XAI) to shed light on how a model use predictors to arrive at a prediction. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models.
  arXiv  Detail & Related papers  (2022-05-11T09:11:02Z)
• Improving Generalization via Uncertainty Driven Perturbations [107.45752065285821]
  We consider uncertainty-driven perturbations of the training data points. Unlike loss-driven perturbations, uncertainty-guided perturbations do not cross the decision boundary. We show that UDP is guaranteed to achieve the robustness margin decision on linear models.
  arXiv  Detail & Related papers  (2022-02-11T16:22:08Z)
• Interpretable Data-Based Explanations for Fairness Debugging [7.266116143672294]
  Gopher is a system that produces compact, interpretable, and causal explanations for bias or unexpected model behavior. We introduce the concept of causal responsibility that quantifies the extent to which intervening on training data by removing or updating subsets of it can resolve the bias. Building on this concept, we develop an efficient approach for generating the top-k patterns that explain model bias.
  arXiv  Detail & Related papers  (2021-12-17T20:10:00Z)
• Disentangling Generative Factors of Physical Fields Using Variational Autoencoders [0.0]
  This work explores the use of variational autoencoders (VAEs) for non-linear dimension reduction. A disentangled decomposition is interpretable and can be transferred to a variety of tasks including generative modeling.
  arXiv  Detail & Related papers  (2021-09-15T16:02:43Z)
• Discrete Denoising Flows [87.44537620217673]
  We introduce a new discrete flow-based model for categorical random variables: Discrete Denoising Flows (DDFs) In contrast with other discrete flow-based models, our model can be locally trained without introducing gradient bias. We show that DDFs outperform Discrete Flows on modeling a toy example, binary MNIST and Cityscapes segmentation maps, measured in log-likelihood.
  arXiv  Detail & Related papers  (2021-07-24T14:47:22Z)
• Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators [0.0]
  We propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. The approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $1000times$ reduction in run time.
  arXiv  Detail & Related papers  (2021-06-17T17:04:42Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)

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.