Explainable AI-Based Interface System for Weather Forecasting Model

• URL: http://arxiv.org/abs/2504.00795v1
• Date: Tue, 01 Apr 2025 13:52:34 GMT
• Title: Explainable AI-Based Interface System for Weather Forecasting Model
• Authors: Soyeon Kim, Junho Choi, Yeji Choi, Subeen Lee, Artyom Stitsyuk, Minkyoung Park, Seongyeop Jeong, Youhyun Baek, Jaesik Choi,
• Abstract summary: This study defines three requirements for explanations of black-box models in meteorology through user studies.<n> Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively.<n>Results indicate that the explanations increase decision utility and user trust.
• Score: 21.801445160287532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.

Related papers

• IMPACTX: Improving Model Performance by Appropriately predicting CorrecT eXplanations [0.0]
  IMPACTX is a novel approach that leverages XAI as a fully automated attention mechanism.<n>It provides proper feature attribution maps for the model's decisions, without relying on external XAI methods.
  arXiv  Detail & Related papers  (2025-02-17T14:15:20Z)
• Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
  Support of artificial intelligence based decision-making is a key element in future 6G networks. In such applications, using AI as black-box models is risky and challenging. We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
  arXiv  Detail & Related papers  (2024-07-09T16:24:21Z)
• How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey [48.97104365617498]
  The emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development.
  arXiv  Detail & Related papers  (2024-03-21T15:44:56Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• 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)
• Strategies to exploit XAI to improve classification systems [0.0]
  XAI aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. Most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system.
  arXiv  Detail & Related papers  (2023-06-09T10:38:26Z)
• Characterizing the contribution of dependent features in XAI methods [6.990173577370281]
  We propose a proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity.
  arXiv  Detail & Related papers  (2023-04-04T11:25:57Z)
• Optimizing Explanations by Network Canonization and Hyperparameter Search [74.76732413972005]
  Rule-based and modified backpropagation XAI approaches often face challenges when being applied to modern model architectures. Model canonization is the process of re-structuring the model to disregard problematic components without changing the underlying function. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures.
  arXiv  Detail & Related papers  (2022-11-30T17:17:55Z)
• 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)
• Explanation-by-Example Based on Item Response Theory [0.0]
  This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.
  arXiv  Detail & Related papers  (2022-10-04T14:36:33Z)
• 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)

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.