Safety

A primary concern is the emergence of human-level or superhuman generative models, commonly referred to as AGI, and their potential existential or catastrophic risks to humanity. Connected to that, AI safety aims at avoiding deceptive or power-seeking machine behavior, model self-replication, or shutdown evasion. Ensuring controllability, human oversight, and the implementation of red teaming measures are deemed to be essential in mitigating these risks, as is the need for increased AI safety research and promoting safety cultures within AI organizations instead of fueling the AI race. Furthermore, papers thematize risks from unforeseen emerging capabilities in generative models, restricting access to dangerous research works, or pausing AI research for the sake of improving safety or governance measures first. Another central issue is the fear of weaponizing AI or leveraging it for mass destruction, especially by using LLMs for the ideation and planning of how to attain, modify, and disseminate biological agents. In general, the threat of AI misuse by malicious individuals or groups, especially in the context of open-source models, is highlighted in the literature as a significant factor emphasizing the critical importance of implementing robust safety measures.