The Application of Generative Models in Intelligent Decision Support

Dear Colleagues,

Generative models have seen rapid developments within artificial intelligence, particularly with the advent of diffusion models for image generation, large language models, and other generative techniques. These models have the potential to significantly enhance Decision Support Systems (DSS) by providing human-like analysis, summarization, data exploration, and generation of natural language insights. However, there remain many open challenges in effectively integrating generative AI into decision workflows and ensuring the reliability, robustness, and trustworthiness of AI-assisted decision-making. This Special Issue aims to explore the integration of generative models with DSS, shedding light on how these technologies can be leveraged to create more robust, adaptive, and intelligent decision-making frameworks.

The primary objectives of this Special Issue are as follows:

1. Highlighting the innovations generative AI brings to decision-making processes. Generative AI introduces dynamic, data-driven insights, and predictive capabilities that can significantly improve decision accuracy and adaptability compared to traditional DSS.
2. Showcasing the diverse applications of generative models across various domains. The ability of generative models to simulate scenarios, generate synthetic data, and provide nuanced insights can transform decision support across various sectors, from healthcare to finance.
3. Addressing emerging challenges related to the integration of generative AI in DSS. The integration of generative AI in DSS brings forth challenges such as data quality, interpretability, and ethical considerations, which this Special Issue will aim to address.

We welcome high-quality submissions that explore the following topics (and similar):

1. Theoretical Advances: novel generative models for decision support, improvements in model interpretability and explainability, and techniques for enhancing the robustness and reliability of generative AI in decision-making contexts.
2. Applications in Specific Domains: healthcare, finance, manufacturing, and smart cities, showcasing how generative AI can enhance decision support in these areas.
3. Methodological Approaches: integrating generative AI with traditional DSS, frameworks for real-time decision support, and strategies for incorporating multi-modal data in generative AI-based decision systems.
4. Ethical and Practical Considerations: addressing biases and ensuring fairness, data privacy and security, and case studies on the deployment and operational challenges of generative IDSS.
5. Future Trends and Perspectives: forecasting the impact of generative AI on the evolution of IDSS, cross-disciplinary approaches, and speculations on the next generation of decision support systems driven by generative AI.

Prof. Dr. Haihong E
Dr. Yifan Zhu
Dr. Qika Lin
Dr. Kaize Shi
Guest Editors

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• generative models
• decision support systems
• intelligent decision support
• trustworthy AI