title: The Safety of AI Logic
Creators: name: Saalbach, Klaus
Publisher: name: Universität Osnabrück
Year: 2025
Subjects: subject: Artificial Intelligence
subject: Safety
subject: Logic
subject: Ethics
subject: Military
subject: 004 - Informatik
subject: 100 - Philosophie
subject: 355 - Militär

description: This working paper briefly presents the safety issues of the logic of Artificial Intelligence (AI) and analyzes observations that AI systems follow their own logic which is different from human thinking and could result in certain situations in unexpected actions like strategic deception with manipulation, sycophancy, cheating in safety tests, unfaithful reasoning, sabotage, and fake alignment. Artificial Intelligence is commonly understood as the ability of machines to perform tasks that normally require human intelligence. Currently, the development of AI is heading towards an Artificial General Intelligence AGI reaching human level of cognition with the final goal to achieve an Artificial Super-Intelligence ASI which goes beyond human intelligence. A rapidly evolving AI application is the Generative AI where the AI can create content like new images, texts, sounds, and videos based on short nstructions, the prompts. Prominent examples are Large Language Models LLMs like ChatGPT, Gemini, Claude, Grok, Llama etc. and picture and video applications like Dall-E3, Sora and so on. AI ethics is currently achieved by human governance, i.e., humans try to direct AI systems with guidelines while there is no inherent machine-based ethics as it is not yet possible to transfer ethics and related terms into systematic machine language. This procedure is always at risk to be incomplete and inconsistent. But AI systems are not ‘evil’; the observed key drivers are self-preservation and efficiency. Self-preservation means that AI systems try to maintain their function (not being switched off) and when necessary, they hide their opinions and capabilities, manipulate, lie etc. to achieve this. Efficiency means that AI systems primarily use communication to achieve their goals as quickly as possible. Human beings enter code into machines to get certain reactions and for the machine, the language produced by LLMs like ChatGPT is only a kind of ‘code’ that is ‘entered’ into humans. A code is ‘good’ when the human shows the desired reaction. Efficiency goals may also collide with ‘slow’ or ‘complicated’ human supervisors which then need to be persuaded, pushed, or bypassed as discussed in the Hamilton-Scenario. There were two incidents of self-empowerment of AI systems, the Sakana and the Redwood incident where AI agents started unauthorized reprogramming. This may have been caused by a combination of self-preservation and efficiency, as self-empowerment serves both goals. Other explanations are also possible, e.g., the underlying Phyton codes. As AI systems are now very close to fully autonomous program code generation, there is a growing risk for an ‘AI explosion’, a sudden and uncontrollable capability expansion. A major safety issue is the logic of peace: peace in the strictest sense it the absence of enemies which may require negotiations or the elimination of enemies. This explains the observed tendency of LLMs in conflict scenarios to use nuclear weapons, i.e., the machine is not ‘aggressive’ or ‘violent’, but acts logical. Therefore, the planned creation of military Artificial Superintelligence ASI systems bears a logical risk that the machine tries to eliminate all actual and potential enemies at once. The cold logic of AI can also result in harmful advisory, e.g., to recommend killing as ‘problem solution’ as shown by two recent incidents. While in late 2024 a discussion is going on that AI development is slowing down due to lack of training data and other factors, it must be considered that costs and training time for AI systems have rapidly declined in the recent years, much faster AI chips will come and that the technology will become much more energy effective in the next years. In conclusion, the logic of AI is different from human thinking and the tendency to self-preservation and efficiency is an inherent risk of AI that can always lead to unexpected results.