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Jun 2, 2025
Leveraging Benford’s Law for Computational Complexity and Routing Transparency in AI Systems with Cryptographic Timestamps and Event-Triggered Actions
Aaron Goulden
Summary
This paper proposes an innovative method for embedding computational transparency into AI systems through cryptographic timestamping, Benford’s Law deviation analysis, and event-driven actions. By analyzing the deviation from Benford’s Law in timestamped logs, we derive insights into the computational complexity of query routing and the specific path a query took through a distributed network of trusted, semi-trusted, and untrusted nodes. This method not only enhances the traceability and legitimacy of AI outputs but also introduces the concept of triggering event-based actions, such as compensating analysts or deploying security teams, depending on the sensitivity of the models accessed. We discuss how this system ensures ethical data use, detects potential misuse of AI models, and provides an automated response to high-risk scenarios, offering a transparent and auditable framework for AI operations.
Cite this work:
@misc {
title={
},
author={
Aaron Goulden
},
date={
6/2/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
