Formal Abductive Latent Explanations for Prototype-Based Networks

• URL: http://arxiv.org/abs/2511.16588v1
• Date: Thu, 20 Nov 2025 17:42:41 GMT
• Title: Formal Abductive Latent Explanations for Prototype-Based Networks
• Authors: Jules Soria, Zakaria Chihani, Julien Girard-Satabin, Alban Grastien, Romain Xu-Darme, Daniela Cancila,
• Abstract summary: Case-based reasoning networks make predictions based on similarity between the input and prototypical parts of training samples, called prototypes.<n>We show that such explanations are sometimes misleading, which hampers their usefulness in safety-critical contexts.<n>We propose Abductive Latent Explanations (ALEs), a formalism to express sufficient conditions on the intermediate representation of the instance that imply the prediction.
• Score: 7.001970497421476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes. Such models are able to explain each decision by pointing to the prototypes that contributed the most to the final outcome. As the explanation is a core part of the prediction, they are often qualified as ``interpretable by design". While promising, we show that such explanations are sometimes misleading, which hampers their usefulness in safety-critical contexts. In particular, several instances may lead to different predictions and yet have the same explanation. Drawing inspiration from the field of formal eXplainable AI (FXAI), we propose Abductive Latent Explanations (ALEs), a formalism to express sufficient conditions on the intermediate (latent) representation of the instance that imply the prediction. Our approach combines the inherent interpretability of case-based reasoning models and the guarantees provided by formal XAI. We propose a solver-free and scalable algorithm for generating ALEs based on three distinct paradigms, compare them, and present the feasibility of our approach on diverse datasets for both standard and fine-grained image classification. The associated code can be found at https://github.com/julsoria/ale

Related papers

• From Black-box to Causal-box: Towards Building More Interpretable Models [57.23201263629627]
  We introduce the notion of causal interpretability, which formalizes when counterfactual queries can be evaluated from a specific class of models.<n>We derive a complete graphical criterion that determines whether a given model architecture supports a given counterfactual query.
  arXiv  Detail & Related papers  (2025-10-24T20:03:18Z)
• I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? [76.15163242945813]
  Large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.<n>We introduce a novel generative model that generates tokens on the basis of human-interpretable concepts represented as latent discrete variables.
  arXiv  Detail & Related papers  (2025-03-12T01:21:17Z)
• Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval [139.21955930418815]
  Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
  arXiv  Detail & Related papers  (2023-09-29T09:41:19Z)
• Understanding Post-hoc Explainers: The Case of Anchors [6.681943980068051]
  We present a theoretical analysis of a rule-based interpretability method that highlights a small set of words to explain a text's decision. After formalizing its algorithm and providing useful insights, we demonstrate mathematically that Anchors produces meaningful results.
  arXiv  Detail & Related papers  (2023-03-15T17:56:34Z)
• Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces [14.70409833767752]
  Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. We propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis.
  arXiv  Detail & Related papers  (2022-12-30T18:04:25Z)
• Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling [7.376829794171344]
  We propose a Self-Explaining Selective Model (SESM) that uses a linear combination of prototypical concepts to explain its own predictions. For better interpretability, we design multiple constraints including diversity, stability, and locality as training objectives.
  arXiv  Detail & Related papers  (2022-12-07T01:42:47Z)
• 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)
• 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)
• Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
  We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
  arXiv  Detail & Related papers  (2020-11-11T05:58:17Z)

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.