VISHIEN-MAAT: Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

• URL: http://arxiv.org/abs/2304.03288v1
• Date: Tue, 4 Apr 2023 08:26:54 GMT
• Title: VISHIEN-MAAT: Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users
• Authors: Noptanit Chotisarn, Sarun Gulyanon, Tianye Zhang, Wei Chen
• Abstract summary: This work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch.
• Score: 8.939421900877742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning. AI technology has been applied in almost every field; therefore, technical and non-technical end-users must understand these technologies to exploit them. However existing materials are designed for experts, but non-technical users need appealing materials that deliver complex ideas in easy-to-follow steps. One notable tool that fits such a profile is scrollytelling, an approach to storytelling that provides readers with a natural and rich experience at the reader's pace, along with in-depth interactive explanations of complex concepts. Hence, this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. As a demonstration of our design, we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch. The results show that the visualization based on our novel design helps improve non-technical users' perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

Related papers

• Fiper: a Visual-based Explanation Combining Rules and Feature Importance [3.2982707161882967]
  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.
  arXiv  Detail & Related papers  (2024-04-25T09:15:54Z)
• 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)
• Recent Trends in 3D Reconstruction of General Non-Rigid Scenes [104.07781871008186]
  Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. This state-of-the-art report (STAR) offers the reader a comprehensive summary of state-of-the-art techniques with monocular and multi-view inputs.
  arXiv  Detail & Related papers  (2024-03-22T09:46:11Z)
• The role of interface design on prompt-mediated creativity in Generative AI [0.0]
  We analyze more than 145,000 prompts from two Generative AI platforms. We find that users exhibit a tendency towards exploration of new topics over exploitation of concepts visited previously.
  arXiv  Detail & Related papers  (2023-11-30T22:33:34Z)
• Artistic Strategies to Guide Neural Networks [0.0]
  This paper explores the potentials and limits of current AI technology, in the context of image, text, form and translation of semiotic spaces. In a relatively short time, the generation of high-resolution images and 3D objects has been achieved. Yet again, we see how artworks act as catalysts for technology development.
  arXiv  Detail & Related papers  (2023-07-06T22:57:10Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• Generative Transformers for Design Concept Generation [7.807713821263175]
  This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field. A novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis.
  arXiv  Detail & Related papers  (2022-11-07T11:29:10Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview [72.79089075263985]
  Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors.
  arXiv  Detail & Related papers  (2022-02-28T06:28:44Z)
• 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)

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.