Causal and Explainable Artificial Intelligence
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 6171
Special Issue Editors
Interests: interpretable/explainable AI; causal discovery and inference; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the last decade, machine learning (ML) and artificial intelligence (AI) have been increasingly adopted in various domains. However, the lack of transparency and interpretability in AI/ML models has resulted in a growing demand to make them more understandable to humans. This is crucial for ensuring effective collaboration between humans and AI systems and for ensuring regulatory compliance.
Based on the Causal and Explainable AI (CXAI 2023) workshop, this Special Issue will present a collection of cutting-edge research and recent real-world applications in causal inference/discovery and interpretable/explainable AI, with the aim of making complex or black-box AI/ML models understandable and supporting reliable, trustable and responsible decision making.
Authors are encouraged to submit their papers to the CXAI 2023 workshop first and, if accepted, then submit extended versions of their papers to this Special Issue. Alternatively, authors may submit their papers directly to the Special Issue, without submitting to the CXAI workshop.
Dr. Yanchang Zhao
Dr. Yun-Sing Koh
Guest Editors
Manuscript Submission Information
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Keywords
- interpretable machine learning
- explainable artificial intelligence
- causal discovery
- causal inference
- counterfactual analysis
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