Show HN: Asimov's 4 Laws for AI Contexts (github.com)

A new framework called asimov4laws has been launched, operationalizing Isaac Asimov's Four Laws of Robotics for Large Language Models (LLMs) and autonomous AI agents. This production-grade, pure-Markdown framework employs Expected Harm Calculus (EHC-4) to create a mathematically rigorous safety architecture capable of prompt-injection resistance across various LLM environments. Key features include strict lexicographic dominance prioritizing human safety, dynamic inaction symmetry preventing paralysis in decision-making, and protocols for engaging human operators during uncertainty. The significance of asimov4laws lies in its comprehensive approach to AI safety, providing a structured method to guard against potential risks associated with LLMs and autonomous agents. By defining human and synthetic entities within a clear ontology, the framework aims to ensure that safety measures remain effective regardless of the underlying platform. Its zero-dependency structure enhances portability, making it accessible for developers and researchers to integrate into their systems. As AI alignment and safety continue to be pressing concerns, this framework invites collaboration and critique from the community, encouraging a collective effort to bolster responsible AI development.