Blog | Governance Ledger

The Governance Ledger is the commentary and analysis platform of The Center for Ethical AI.

It publishes structured insights, critical reflections, and applied perspectives on AI governance, accountability, and institutional oversight. Contributions focus on translating ethical principles into operational governance, examining emerging risks, and interrogating real-world AI deployments through a standards-aligned lens.

While distinct from the peer-reviewed AI Governance Review, all content in The Governance Ledger is curated under editorial oversight and aligned with the Center’s governance frameworks. The blog serves as a space for timely analysis, practitioner insight, and governance-focused discourse that informs research, policy, and institutional decision-making.

All Posts

AI Model Autophagy:

When AI Systems Consume Themselves Dale Rutherford, Ph.D. April 7, 2026 The Problem Hiding in Plain Sight Every time a large language model is retrained, it consumes a corpus assembled from the internet. And the internet increasingly is large language model output. Research repositories, news synthesis layers, social media summarizers, knowledge bases -- model-generated text propagates into every channel from which training data is harvested. When that data feeds the next tr

Dale Rutherford

Apr 77 min read

Why AI Governance Needs a Quality Control Revolution

By Dale Rutherford, PhD | AI Governance Architect & Researcher March 2026 Every industry that has achieved reliable, scalable quality did so by treating its production systems as measurable processes. Semiconductor fabrication, pharmaceutical manufacturing, aerospace engineering: each of these domains reached maturity only when practitioners stopped relying on inspection after the fact and started governing the process itself. The tools that enabled this transformation are we

Dale Rutherford

Mar 276 min read

When Words Drift: Lexical Ambiguity and the Quiet Fracture of Meaning in LLMs

By: Dale Rutherford February 6, 2026 Much of the public conversation about Large Language Model reliability focuses on hallucinations, bias, or data provenance. These are visible failures. Far less attention is paid to a quieter, more insidious mechanism that degrades output quality even when models appear fluent, consistent, and confident: lexical ambiguity. In a recent discussion on context windows and epistemic drift, we examined how bounded memory reshapes what a model ca

Dale Rutherford

Feb 64 min read