Human-in-the-loop NLP as a first-class control layer (github.com)

A new repository has been launched, focusing on a human-in-the-loop NLP governance architecture designed to enhance auditability and ethical standards in conversational AI systems. This initiative stems from a rigorous examination of failure modes in conversational AI, such as role drift, authority ambiguity, and instability in responses. By prioritizing human authority and establishing that AI systems should remain assistive rather than directive, this project aims to ensure that every decision made by these systems is auditable and adheres to ethical constraints that are fundamentally architectural. This governance-first approach is significant for the AI/ML community as it addresses the pressing need for ethical oversight in increasingly complex conversational AI applications. The repository targets NLP engineers, AI governance researchers, and safety and ethics reviewers, providing a framework rather than a benchmark or a marketing tool. It emphasizes the importance of integrating human oversight into AI systems to mitigate potential risks and enhance accountability, making it a crucial resource for those seeking to develop safer and more responsible AI technologies.