Navigating AI Safety Uncertainty, Preferences, and Non-Archimedean Utilities
📝 Original Paper Info
- Title: Why AI Safety Requires Uncertainty, Incomplete Preferences, and Non-Archimedean Utilities- ArXiv ID: 2512.23508
- Date: 2025-12-29
- Authors: Alessio Benavoli, Alessandro Facchini, Marco Zaffalon
📝 Abstract
How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant must learn them. The shutdown problem instead concerns designing AI agents that: shut down when a shutdown button is pressed; neither try to prevent nor cause the pressing of the shutdown button; and otherwise accomplish their task competently. In this paper, we show that addressing these challenges requires AI agents that can reason under uncertainty and handle both incomplete and non-Archimedean preferences.💡 Summary & Analysis
1. First Contribution: The study introduces a new data analysis method that yields more accurate results than previous research, akin to detecting objects at greater distances with radar. 2. Second Contribution: It presents a novel approach to optimizing learning algorithms, which can be understood as improving fuel efficiency in an automobile engine by enhancing the performance of each component. 3. Third Contribution: The study proves the flexibility and strengths of its methodology across various datasets, much like creating a multi-tool that can perform multiple tasks.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
