Interactivity and Control

AI Model

Agnostic Neural Network Reinforcement Learning White Box

AI Task

Behaviour Learning Binary classification Estimation Image Classification Multi-class Classification Regression

Application Domain

Artificial Intelligence and Robotics Systems Education Finance/Economics General Health Media and Communication

Type of Users

AI experts Generic Non-experts

Explanation Modality

Text Visual

XAI Model

Counter-exemplars/factual Decision Rules Exemplars Features Importance None

Related Papers

• Hao, J., Shi, Q., Ye, Y., & Zeng, W. (2024). TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations. IEEE Transactions on Visualization and Computer Graphics, 30(1), 1183–1193. https://doi.org/10.1109/tvcg.2023.3327389
• 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
• Guo, L., Daly, E. M., Alkan, O., Mattetti, M., Cornec, O., & Knijnenburg, B. (2022). Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules. In 27th International Conference on Intelligent User Interfaces (pp. 537–548). IUI ’22: 27th International Conference on Intelligent User Interfaces. ACM. https://doi.org/10.1145/3490099.3511111
• Nakao, Y., Stumpf, S., Ahmed, S., Naseer, A., & Strappelli, L. (2022). Toward Involving End-users in Interactive Human-in-the-loop AI Fairness. ACM Transactions on Interactive Intelligent Systems, 12(3), 1–30. https://doi.org/10.1145/3514258
• 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

Effective XUIs empower users by supporting active engagement with the system. Supporting interactive exploration was found useful for users, for example allowing selection, filtering, justapoxition, and smooth transitions between views [ 10.1109/TVCG.2023.3327389, 10.1109/TVCG.2020.3030418]. Moreover, features such as what—if exploration, guided navigation, and real-time updates were found to enhance user engagement and comprehension [10.1109/PacificVis48177.2020.7090, 10.1145/3510003.3510129]. Interaction is also a core requirement for white-box models to iteratively refine the understanding of explanations [10.1145/3490099.3511111 , 10.1145/3514258]. Regarding reinforcement learning systems, interaction modalities such as questioning the model and navigating through explanation spaces help users clarify why actions were taken, increasing trust and understanding of the system [10.1109/PacificVis53943.2022.00020].