This special track will investigate the impacts of LLMs and other Generative AI Models (GAIs) on our daily life and how regulators can respond to ensure that GAIs make sound recommendations/decisions, for the benefits of humanity and societies. In particular, we would like to focus on climate-related decisions and neurodegenerative disease diagnostics.

ChatGPT, the popular chatbot of OpenAI, has been accessed by more than 100 million users monthly the first two months after launching, making it the fastest-growing consumer application. Its phenomenal success attracts substantial business and public interests. LLMs continues to break boundaries and are widely applied in different fields, e.g., education, healthcare, law/public policy, etc., tremendously benefitting citizens of different communities and facilitating public decision-making. However, results/decisions generated by LLMs/GAIs have demonstrated to be equally confusing and biased when the training datasets are distorted or unevenly distributed across different sub-populations. A deeper understanding of the potentials and limitations of LLMs/GAIs is critical for the future advancement of LLMs/GAIs, and the future sustainability of humanity and societies.

This special track aims to explore the technical and associated moral and ethical challenges posed by LLMs/GAIs, and what moral and ethical guidelines should be in place for addressing these challenges. We will explore the trustworthiness and explainability of existing LLMs/GAIs, and when LLMs/GAIs can be considered trustworthy and explainable. Are there any specific moral and ethical guidelines available to guide the future development of LLMs/GAIs? In particular, we would like to focus on climate-related decisions and neurodegenerative disease diagnostics.

The key themes below cover the technical and the associated ethical and moral challenges of LLMs/GAIs, and the opportunities that these models presented for socially beneficial, moral and ethical decision-making, covering, but not limited to, the following topics:

• Challenges of LLMs/GAIs in decision-making, specifically on climate-related decisions and neurodegenerative disease diagnostics
• Regulatory frameworks and guiding principles of LLMs/GAIs for sound decision making on the above specified topics
• Methods for determining if LLMs/GAIs are making socially beneficial, moral and ethical decisions on the above specified topics
• Methods for evaluating trustworthy and explainable LLMs/GAIs on the above specified topics
• Methods to reduce data/model biases and improve fairness on the above specified topics
• Methods to reduce disinformation/misinformation and improve trustworthiness on the above specified topics
• Methods to improve data privacy and security on the above specified topics