Emerging Technologies in Explainable Artificial Intelligence (XAI) for Big Data

Dear Colleagues,

Artificial intelligence applications, methods, techniques, and approaches are rapidly advancing. Although they contribute positively to many aspects of our lives, there are major concerns about how they process and learn from our information, their critical decision making, etc. There is increasing demand from individuals in societies, businesses, organizations, and government bodies to increase our understanding of AI applications in performing tasks. For example, ChatGPT raised concerns from practitioners in many industries (e.g., education) and even prompted certain governments around the world (e.g., Italy) to ban its use by the public. The need for explainable AI has prompted researchers, businesses, and governments to take action to increase form theories and application designs that allow transparency and explanations of the processes and decisions of AI algorithms and products. Thus, this Special Issue invites the contribution of original theoretical or applied research, as well as survey papers, related to methods, approaches, and techniques to explain AI in relation to big data.

The aim of this Special Issue is to invite, encourage, and disseminate original research contributions to expand our knowledge boundary on explainable AI and its applications to big data.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Theory and foundations of explainable AI using big data;
2. Abstraction, validation, and generalization of explainable AI;
3. Methods and standards for explainable AI;
4. Explainable AI approaches to natural language processing, computer vision, image processing, robotics, cybersecurity, etc.
5. Analyses of AI or machine learning model training;
6. Explainable machine learning algorithms;
7. Explainable AI training processes or model representations;
8. Literature reviews on recent trends, methods, and approaches to explainable AI;
9. Challenges and solutions in the design and application of explainable AI;
10. User trust and privacy issue in AI and machine learning models;
11. Assessment and impact of national and international laws and regulations on explainable AI;
12. Artificial intelligence and machine learning for the security and privacy of big data;
13. Artificial intelligence, safety assurance, and reliability of big data;
14. Artificial intelligence and intrusion detection for big data;
15. Artificial intelligence and blockchain for big data.

Dr. Sardar Jaf
Dr. Qiang Huang
Dr. Ibrahim Ghafir
Guest Editors

Manuscript Submission Information

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