This repository provides an innovative human-in-the-loop governance architecture designed for Natural Language Processing (NLP) applications. The focus is on ensuring auditability, role stability, and ethical safeguards specifically tailored for conversational AI systems.

Overview

The architecture has been developed based on a comprehensive analysis of common failure modes in conversational AI, pinpointing issues such as role drift, authority ambiguity, and post-hoc instability of responses. For an in-depth examination, a full analysis is available in the whitepaper.

Core Principles

The project is grounded on essential principles that promote safe and ethical usage of AI technologies:

• Human authority is non-negotiable: Emphasizing the importance of human control over AI systems.
• AI systems must remain assistive, not directive: Ensuring AI functions as a support tool rather than making unilateral decisions.
• All decisions must be auditable: Transparency is key; every decision made must be traceable.
• Ethical constraints are architectural, not optional: Ethical considerations are integrated into the design rather than treated as add-ons.

Target Audience

This repository is especially relevant for:

• NLP engineers looking to implement governance in their models.
• AI governance researchers focused on the regulatory aspects of artificial intelligence.
• Safety and ethics reviewers examining ethical implications of AI.
• Architects of multi-agent systems seeking to enhance accountability and stability.

Clarification of Purpose

It is important to note what this project is not:

• It is not a benchmark for performance evaluation.
• It does not serve as a chatbot demonstration.
• It is not a tool for marketing purposes.

This project represents a governance-first technical exploration in the NLP domain, aiming to reshape how conversational AI can be utilized ethically and effectively.