A Review of Safety-Ethical Issues and Governance Approaches for Large Language Models

Abstract

The large pre-trained models centered on the Transformer architecture have been widely applied in fields such as government affairs, healthcare, and cultural creation, significantly enhancing the efficiency of content production. However, they have also given rise to multiple security and ethical risks: Data leakage can lead to the leakage of users' medical records privacy, attackers can steal commercial large models through API calls, multi-modal deep fakes are used in telecommunications fraud, and algorithmic biases in similar Amazon recruitment large models can cause employment discrimination. This paper takes generative large models as the research object and adopts methods such as literature review, case analysis, and comparative research to classify the risks of large models into two categories: information security and ethical security. It summarizes the protection technologies throughout the entire life cycle of training, reasoning, and traceability, compares the differences in AI regulatory systems between China and abroad, and constructs a complete governance path from the technical, institutional, and industrial dimensions. This paper can provide a basic theoretical reference for the safe implementation of large model enterprises and the improvement of industry norms by regulatory authorities.