Rashomon in the Streets: Explanation Ambiguity in Scene Understanding

• URL: http://arxiv.org/abs/2509.03169v1
• Date: Wed, 03 Sep 2025 09:36:18 GMT
• Title: Rashomon in the Streets: Explanation Ambiguity in Scene Understanding
• Authors: Helge Spieker, Jørn Eirik Betten, Arnaud Gotlieb, Nadjib Lazaar, Nassim Belmecheri,
• Abstract summary: Rashomon effect is where multiple, equally accurate models can offer divergent explanations for the same prediction.<n>This paper provides the first empirical quantification of this effect for the task of action prediction in real-world driving scenes.<n>Our findings suggest that explanation ambiguity is an inherent property of the problem, not just a modeling artifact.
• Score: 6.218742688352911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable AI (XAI) is essential for validating and trusting models in safety-critical applications like autonomous driving. However, the reliability of XAI is challenged by the Rashomon effect, where multiple, equally accurate models can offer divergent explanations for the same prediction. This paper provides the first empirical quantification of this effect for the task of action prediction in real-world driving scenes. Using Qualitative Explainable Graphs (QXGs) as a symbolic scene representation, we train Rashomon sets of two distinct model classes: interpretable, pair-based gradient boosting models and complex, graph-based Graph Neural Networks (GNNs). Using feature attribution methods, we measure the agreement of explanations both within and between these classes. Our results reveal significant explanation disagreement. Our findings suggest that explanation ambiguity is an inherent property of the problem, not just a modeling artifact.

Related papers

• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Probing Graph Representations [77.7361299039905]
  We use a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We advocate for probing as a useful diagnostic tool for evaluating graph-based models.
  arXiv  Detail & Related papers  (2023-03-07T14:58:18Z)
• Towards Explainable Motion Prediction using Heterogeneous Graph Representations [3.675875935838632]
  Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. GNN-based approaches have recently gained attention as they are well suited to naturally model these interactions. In this work, we aim to improve the explainability of motion prediction systems by using different approaches.
  arXiv  Detail & Related papers  (2022-12-07T17:43:42Z)
• MEGAN: Multi-Explanation Graph Attention Network [1.1470070927586016]
  We propose a multi-explanation graph attention network (MEGAN) Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels. Our attention-based network is fully differentiable and explanations can actively be trained in an explanation-supervised manner.
  arXiv  Detail & Related papers  (2022-11-23T16:10:13Z)
• Motif-guided Time Series Counterfactual Explanations [1.1510009152620664]
  We propose a novel model that generates intuitive post-hoc counterfactual explanations. We validated our model using five real-world time-series datasets from the UCR repository.
  arXiv  Detail & Related papers  (2022-11-08T17:56:50Z)
• CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
  We study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed. We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
  arXiv  Detail & Related papers  (2022-10-16T04:35:32Z)
• Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification [6.940242990198]
  Greybox XAI is a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB) We address the problem of the lack of universal criteria for XAI by formalizing what an explanation is. We show how this new architecture is accurate and explainable in several datasets.
  arXiv  Detail & Related papers  (2022-09-26T08:55:31Z)
• Reinforced Causal Explainer for Graph Neural Networks [112.57265240212001]
  Explainability is crucial for probing graph neural networks (GNNs) We propose a reinforcement learning agent, Reinforced Causal Explainer (RC-Explainer) RC-Explainer generates faithful and concise explanations, and has a better power to unseen graphs.
  arXiv  Detail & Related papers  (2022-04-23T09:13:25Z)
• GLIME: A new graphical methodology for interpretable model-agnostic explanations [0.0]
  This paper contributes to the development of a novel graphical explainability tool for black box models. The proposed XAI methodology, termed as gLIME, provides graphical model-agnostic explanations either at the global (for the entire dataset) or the local scale (for specific data points)
  arXiv  Detail & Related papers  (2021-07-21T08:06:40Z)
• 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)
• Dependency Decomposition and a Reject Option for Explainable Models [4.94950858749529]
  Recent deep learning models perform extremely well in various inference tasks. Recent advances offer methods to visualize features, describe attribution of the input. We present the first analysis of dependencies regarding the probability distribution over the desired image classification outputs.
  arXiv  Detail & Related papers  (2020-12-11T17:39:33Z)
• Deducing neighborhoods of classes from a fitted model [68.8204255655161]
  In this article a new kind of interpretable machine learning method is presented. It can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed.
  arXiv  Detail & Related papers  (2020-09-11T16:35:53Z)

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.