Navigating explanations
- AI Model
- Agnostic Neural Network Reinforcement Learning Unknown
- AI Task
- Behaviour Learning Image Classification Multi-class Classification
- Application Domain
- Artificial Intelligence and Robotics Systems Education General Health Natural Language Processing
- Type of Users
- AI experts Generic Non-experts
- Explanation Modality
- Text Visual
- XAI Model
- Exemplars Features Importance None
Related Papers
- Khurana, A., Alamzadeh, P., & Chilana, P. K. (2021). ChatrEx: Designing Explainable Chatbot Interfaces for Enhancing Usefulness, Transparency, and Trust. In 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 1–11). 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE. https://doi.org/10.1109/vl/hcc51201.2021.9576440
- Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Polo Chau, D. H. (2021). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396–1406. https://doi.org/10.1109/tvcg.2020.3030418
- Collaris, D., & van Wijk, J. J. (2020). ExplainExplore: Visual Exploration of Machine Learning Explanations. In 2020 IEEE Pacific Visualization Symposium (PacificVis) (pp. 26–35). 2020 IEEE Pacific Visualization Symposium (PacificVis). IEEE. https://doi.org/10.1109/pacificvis48177.2020.7090
- Huang, J., Mishra, A., Kwon, B. C., & Bryan, C. (2023). ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective. IEEE Transactions on Visualization and Computer Graphics, 29(1), 831–841. https://doi.org/10.1109/tvcg.2022.3209384
- Mishra, A., Soni, U., Huang, J., & Bryan, C. (2022). Why? Why not? When? Visual Explanations of Agent Behaviour in Reinforcement Learning. In 2022 IEEE 15th Pacific Visualization Symposium (PacificVis) (pp. 111–120). 2022 IEEE 15th Pacific Visualization Symposium (PacificVis). IEEE. https://doi.org/10.1109/pacificvis53943.2022.00020
Users should have freedom and control in accessing and navigating the explanations by including UI controls such as “next”, “previous”, or “exit”.
This can lead to unlock visual step-by-step explanations, which can be more appealing, relatable, and easy to understand [10.1109/VL/HCC51201.2021.9576440]. Explanations can be further improved when combined with animations such as fluid transitions between different-level views to improve engagement, comprehension, and enjoyment [10.1109/TVCG.2020.3030418].
Presenting explanations in multiple “pages” or segments allows users to control the depth of information they receive, aligning with their cognitive needs and preventing information overload (“Incremental Information Disclosure”) [10.1145/3627043.3659566].
Navigation can also happen at feature space to enable “what-if” analyses [10.1109/PacificVis48177.2020.7090, 10.1109/PacificVis53943.2022.00020], especially when datasets are large [10.1109/TVCG.2022.3209384].