Practical Framework

Practical frameworks are emerging to address the challenges of developing and deploying reliable and fair AI systems. Current research focuses on improving the autonomy and scalability of large language model-based multi-agent systems, mitigating bias and ensuring fairness in LLM applications, and establishing standardized methods for documenting and evaluating AI systems, including uncertainty quantification in object detection and structure preservation in medical image enhancement. These frameworks aim to enhance the trustworthiness, efficiency, and regulatory compliance of AI across diverse applications, impacting both the scientific understanding of AI and its practical deployment.