RETRACTED: AI safety practices and public perception: Historical analysis, survey insights, and a weighted scoring framework

摘要

Artificial Intelligence (AI) safety has evolved in tandem with advances in technology and shifts in societal attitudes. This article presents a historical and empirical analysis of AI safety concerns from the mid-twentieth century to the present, integrating archival records, media narratives, survey data, landmark research, and regulatory developments. Early anxieties—rooted in Cold War geopolitics and science fiction—focused on physical robots and autonomous weapons. In contrast, contemporary debates focus on algorithmic bias, misinformation, job displacement, and existential risks posed by advanced systems, such as large language models (LLMs). This article examines the impact of key scholarly contributions, significant events, and regulatory milestones on public perception and governance approaches. Building on this context, this study proposes an improved LLM safety scoring system that prioritizes existential risk mitigation, transparency, and governance accountability. Applying the proposed framework to leading AI developers reveals significant variation in safety commitments. The results underscore how weighting choices affect rankings. Comparative analysis with existing indices highlights the importance of nuanced, multidimensional evaluation methods. The paper concludes by identifying pressing governance challenges, including the need for global cooperation, robust interpretability, and ongoing monitoring of harm in high-stakes domains. These findings demonstrate that AI safety is not static but somewhat shaped by historical context, technical capabilities, and societal values—requiring the continuous adaptation of both policy and evaluation frameworks to align AI systems with human interests. • Provides a historical analysis of AI safety concerns from the mid-20th century to the present. • Synthesizes archival records, media narratives, survey data, research works, and regulatory developments. • Identifies key shifts in public perception from fears of killer robots to concerns about misinformation, bias, and existential risk. • Introduces an improved large language model (LLM) safety scoring system with weighted domains. • Applies the scoring framework to leading AI developers, revealing disparities in safety practices. • Compares results with the Future of Life Institute index to demonstrate the impact of weighting choices. • Highlights policy implications, emphasizing transparency, existential risk mitigation, and global cooperation.