2024/11/21 by Frédéric Berdoz, Roger Wattenhofer, Berdoz, Frédéric +1 · 1 citation
Computer Science · Decision Sciences · Social Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Blockchain Technology Applications and Security #Computers and Society (cs.CY) #Ethics and Social Impacts of AI #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2412.00033
openalex publication_date 2024/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While autonomous agents often surpass humans in their ability to handle vast and complex data, their potential misalignment (i.e., lack of transparency regarding their true objective) has thus far hindered their use in critical applications such as social decision processes. More importantly, existing alignment methods provide no formal guarantees on the safety of such models. Drawing from utility and social choice theory, we provide a novel quantitative definition of alignment in the context of social decision-making. Building on this definition, we introduce probably approximately aligned (i.e., near-optimal) policies, and we derive a sufficient condition for their existence. Lastly, recognizing the practical difficulty of satisfying this condition, we introduce the relaxed concept of safe (i.e., nondestructive) policies, and we propose a simple yet robust method to safeguard the black-box policy of any autonomous agent, ensuring all its actions are verifiably safe for the society.