posted by organizer: rgiot || 2879 views || tracked by 5 users: [display]

BGMV-XAI 2022 : Vis&ML for XAI - Bridging the Gap between ML and Visualization communities for eXplainable Artificial Intelligence -- Special Session of ICPRAI

FacebookTwitterLinkedInGoogle

Link: https://bgmv-xai.labri.fr/
 
When Jun 1, 2022 - Jun 3, 2022
Where Paris
Submission Deadline Jan 15, 2022
Notification Due Mar 8, 2022
Final Version Due Mar 22, 2022
Categories    XAI   visualization   machine learning   deep learning
 

Call For Papers

The rise of machine learning approaches, and in particular deep learning, has led to a significant increase in the performance of AI systems. However, it has also raised the question of the reliability and explicability of their predictions for decision-making (i.e., the black-box issue of the deep models). Such shortcomings also raise many ethical and political concerns that prevent wider adoption of this potentially highly beneficial technology, especially in critical areas, such as healthcare, self-driving cars or security. It is therefore critical to understand how their predictions correlate with information perception and expert decision-making. The objective of eXplainable AI (XAI) is to open this black-box by proposing methods to understand and explain how these systems produce their decisions.

Research work in XAI is currently carried out in parallel by the Machine Learning and the Information Visualization communities using methodologies and competencies from their own field. This special session hosted by the ICPRAI conference, endorsed by IAPR, is an opportunity to fill the gap between Machine Learning and Information Visualization communities and to promote new joint research paths.

Here are the main, but not limited to, topics of interest:

- Trust, Uncertainty, Fairness, Accountability and Transparency
- Explainable/Interpretable Machine Learning
- Information visualization for models or their predictions
- Interactive applications for XAI
- XAI Evaluation and Benchmarks
- Human-AI interface and interaction design
- Sample-centric and Dataset-centric explanations
- Attention mechanisms for XAI
- Pruning with XAI

We expect papers written by researchers from both communities, with a preference for works that imply a joint research (e.g., visualization experts with machine learning experts). Paper selection will be achieved by a program committee of experts in Machine Learning and experts in Information Visualization; additionally, each paper will be reviewed by at least one expert of the two communities.

Related Resources

FLAIRS-36 ST XAI, Bias, and Trust 2023   FLAIRS Special Track on Explainability, Bias, and Trust in Artificial Intelligence
ICDM 2023   International Conference on Data Mining
xAI 2023   1st World Conference on eXplainable Artificial Intelligence
JCRAI 2023-Ei Compendex & Scopus 2023   2023 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2023)
XAI-Healthcare 2023   XAI-Healthcare 2023 : eXplainable Artificial Intelligence in Healthcare workshop
IEEE Xplore-Ei/Scopus-CCCAI 2023   2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI 2023) -EI Compendex
XAI^3 Workshop 2023   Joint workshops on XAI methods, challenges and applications at the 26th European Conference on Artificial Intelligence
JCICE 2024   2024 International Joint Conference on Information and Communication Engineering(JCICE 2024)
JAAMAS-XAI 2023   Special Issue on Multi-Agent Systems and Explainable AI @ JAAMAS
DPPR 2023   13th International Conference on Digital Image Processing and Pattern Recognition