Fiper: a Visual-based Explanation Combining Rules and Feature Importance

• URL: http://arxiv.org/abs/2404.16903v1
• Date: Thu, 25 Apr 2024 09:15:54 GMT
• Title: Fiper: a Visual-based Explanation Combining Rules and Feature Importance
• Authors: Eleonora Cappuccio, Daniele Fadda, Rosa Lanzilotti, Salvatore Rinzivillo,
• Abstract summary: Explainable Artificial Intelligence aims to design tools and techniques to illustrate the predictions of the so-called black-box algorithms. This paper proposes a visual-based method to illustrate rules paired with feature importance.
• Score: 3.2982707161882967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence algorithms have now become pervasive in multiple high-stakes domains. However, their internal logic can be obscure to humans. Explainable Artificial Intelligence aims to design tools and techniques to illustrate the predictions of the so-called black-box algorithms. The Human-Computer Interaction community has long stressed the need for a more user-centered approach to Explainable AI. This approach can benefit from research in user interface, user experience, and visual analytics. This paper proposes a visual-based method to illustrate rules paired with feature importance. A user study with 15 participants was conducted comparing our visual method with the original output of the algorithm and textual representation to test its effectiveness with users.

Related papers

• CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models [1.0855602842179624]
  We present a novel approach that enables domain experts to quickly create concept-based explanations for computer vision tasks intuitively via natural language. The modular design of CoProNN is simple to implement, it is straightforward to adapt to novel tasks and allows for replacing the classification and text-to-image models. We show that our strategy competes very well with other concept-based XAI approaches on coarse grained image classification tasks and may even outperform those methods on more demanding fine grained tasks.
  arXiv  Detail & Related papers  (2024-04-23T08:32:38Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Explaining Deep Face Algorithms through Visualization: A Survey [57.60696799018538]
  This work undertakes a first-of-its-kind meta-analysis of explainability algorithms in the face domain. We review existing face explainability works and reveal valuable insights into the structure and hierarchy of face networks.
  arXiv  Detail & Related papers  (2023-09-26T07:16:39Z)
• Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations [11.667611038005552]
  We take a step back from sophisticated predictive algorithms and look into explainability of simple decision-making models. We aim to assess how people perceive comprehensibility of their different representations. This allows us to capture how diverse stakeholders judge intelligibility of fundamental concepts that more elaborate artificial intelligence explanations are built from.
  arXiv  Detail & Related papers  (2023-03-02T03:15:35Z)
• Towards Involving End-users in Interactive Human-in-the-loop AI Fairness [1.889930012459365]
  Ensuring fairness in artificial intelligence (AI) is important to counteract bias and discrimination in far-reaching applications. Recent work has started to investigate how humans judge fairness and how to support machine learning (ML) experts in making their AI models fairer. Our work explores designing interpretable and interactive human-in-the-loop interfaces that allow ordinary end-users to identify potential fairness issues.
  arXiv  Detail & Related papers  (2022-04-22T02:24:11Z)
• ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning [91.58711082348293]
  Reinforcement learning from online user feedback on the system's performance presents a natural solution to this problem. This approach tends to require a large amount of human-in-the-loop training data, especially when feedback is sparse. We propose a hierarchical solution that learns efficiently from sparse user feedback.
  arXiv  Detail & Related papers  (2022-02-05T02:01:19Z)
• This is not the Texture you are looking for! Introducing Novel Counterfactual Explanations for Non-Experts using Generative Adversarial Learning [59.17685450892182]
  counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image. We present a novel approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Our results show that our approach leads to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems.
  arXiv  Detail & Related papers  (2020-12-22T10:08:05Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)
• Leveraging Rationales to Improve Human Task Performance [15.785125079811902]
  Given a computational system's performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods. Results show that our approach produces rationales that lead to statistically significant improvement in human task performance.
  arXiv  Detail & Related papers  (2020-02-11T04:51:35Z)

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.