Available on Zenodo:

https://doi.org/10.5281/zenodo.16980932

 

This paper introduces Thorp's AI Ethics Graph, a novel, visual methodology designed to bridge this gap by providing a dynamic “ethical fingerprint” of AI systems.
It is built around two complementary diagnostic tools: the Ethical Alignment Hexagon and the Ethical Misalignment Hexagon.  The framework captures both positive ethical development and systemic vulnerabilities, revealing subtle contradictions that single-metric audits or static dashboards overlook.

 

The AI Ethics Graphs are positioned as a third pillar of AI governance, alongside principles and regulations. It intends to advance the field toward actionable transparency, enabling continuous monitoring, early warning for ethical drift, and forensic investigation after incidents. Ultimately, the framework empowers practitioners not by dictating what to believe, but by revealing whether intelligent systems are upholding the values entrusted to them.

By design, Thorp's AI Ethics Graph does not prescribe fixed ethical standards but functions as a universal, configurable tool adaptable to diverse industries and governance contexts.

 

The accelerating integration of artificial intelligence into critical social domains has exposed a persistent “operationalization gap” between high-level ethical principles and their real-world application. While principle-based frameworks provide moral direction and regulations establish legal guardrails, neither offers the continuous, diagnostic visibility needed to understand AI’s evolving ethical state. 

 

Beyond theoretical framing, the modular pipeline for data ingestion, scoring, and visualization ensures practical deployability across technical environments.  Through illustrative test cases (including customer service AI, cybersecurity-compromised systems, and ethically “masked” assistants) the paper demonstrates how the tool enables nuanced, context-driven interpretation.