Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness

• URL: http://arxiv.org/abs/2510.11164v1
• Date: Mon, 13 Oct 2025 08:55:45 GMT
• Title: Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness
• Authors: Ilaria Vascotto, Alex Rodriguez, Alessandro Bonaita, Luca Bortolussi,
• Abstract summary: This paper investigates the role of robustness through the usage of a feature importance aggregation derived from multiple models.<n>Preliminary results showcase the potential in increasing the trustworthiness of the application, while leveraging multiple model's predictive power.
• Score: 43.25173443756643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of Artificial Intelligence (AI) models in real-world and high-risk applications has intensified the discussion about their trustworthiness and ethical usage, from both a technical and a legislative perspective. The field of eXplainable Artificial Intelligence (XAI) addresses this challenge by proposing explanations that bring to light the decision-making processes of complex black-box models. Despite being an essential property, the robustness of explanations is often an overlooked aspect during development: only robust explanation methods can increase the trust in the system as a whole. This paper investigates the role of robustness through the usage of a feature importance aggregation derived from multiple models ($k$-nearest neighbours, random forest and neural networks). Preliminary results showcase the potential in increasing the trustworthiness of the application, while leveraging multiple model's predictive power.

Related papers

• DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay [50.31585196187091]
  We introduce DixitWorld, a comprehensive evaluation suite designed to deconstruct multimodal abductive reasoning.<n>DIXITWORLD features two core components: DixitArena, a dynamic, multi-agent environment that evaluates hypothesis generation and hypothesis selection.<n>Results from DixitArena reveal distinct, role-dependent behaviors.
  arXiv  Detail & Related papers  (2025-10-11T08:48:48Z)
• Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems [49.69773210844221]
  This paper challenges the prevailing hypothesis that persuasive efficacy is primarily a function of model scale.<n>Through a series of multi-agent persuasion experiments, we uncover a fundamental trade-off we term the Persuasion Duality.<n>Our findings reveal that the reasoning process in LRMs exhibits significantly greater resistance to persuasion, maintaining their initial beliefs more robustly.
  arXiv  Detail & Related papers  (2025-09-25T12:03:10Z)
• Explainable artificial intelligence (XAI): from inherent explainability to large language models [0.0]
  Explainable AI (XAI) techniques facilitate the explainability or interpretability of machine learning models.<n>This paper details the advancements of explainable AI methods, from inherently interpretable models to modern approaches.<n>We review explainable AI techniques that leverage vision-language model (VLM) frameworks to automate or improve the explainability of other machine learning models.
  arXiv  Detail & Related papers  (2025-01-17T06:16:57Z)
• When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances [42.36530107262305]
  robustness of explanations plays a central role in ensuring trust in both the system and the provided explanation.<n>We propose a novel approach to analyse the robustness of neural network explanations to non-adversarial perturbations.<n>We additionally present an ensemble method to aggregate various explanations, showing how merging explanations can be beneficial for both understanding the model's decision and evaluating the robustness.
  arXiv  Detail & Related papers  (2024-06-20T14:17:57Z)
• SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction [15.832975722301011]
  We propose a novel method to enhance explainability with minimal accuracy loss. We have developed novel methods for estimating nodes by leveraging AI techniques. Our findings highlight the critical role that statistical methodologies can play in advancing explainable AI.
  arXiv  Detail & Related papers  (2024-06-16T14:43:01Z)
• T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients [5.946429628497358]
  We introduce T-Explainer, a novel additive attribution explainer based on the Taylor expansion.<n>T-Explainer offers desirable properties such as local accuracy and consistency.<n>We provide several tools to evaluate and visualize explanations, turning T-Explainer into a comprehensive XAI framework.
  arXiv  Detail & Related papers  (2024-04-25T10:40:49Z)
• Shapelet-Based Counterfactual Explanations for Multivariate Time Series [0.9990687944474738]
  We develop a model agnostic multivariate time series (MTS) counterfactual explanation algorithm. We test our approach on a real-life solar flare prediction dataset and prove that our approach produces high-quality counterfactuals. In addition to being visually interpretable, our explanations are superior in terms of proximity, sparsity, and plausibility.
  arXiv  Detail & Related papers  (2022-08-22T17:33:31Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• 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)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)

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.