Dale Rutherford
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Researcher
Also affiliated: School for Ethical Education (2026)
Faculty Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Dale Rutherford's research focuses on the governance and operational risks associated with artificial intelligence, particularly large language models (LLMs). His work investigates methods for quantifying 'epistemic decay' in recursive AI training ecosystems and proposes adaptive governance strategies as lifecycle control systems. Rutherford has also explored cybersecurity risks inherent in the adoption and use of digital business cards within organizational and end-user contexts.
Rutherford has 9 publications and 2 citations, with an h-index of 1. He collaborates with Ning Wu at the University of Arkansas at Little Rock, with whom he shares one publication. His most recent publication was in 2026, indicating recent activity in his research areas.
Metrics
- h-index: 1
- Publications: 13
- Citations: 2
Selected Publications
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AI Behavioral Assurance: A Lean Six Sigma Methodology for the LIfecycle Governance of Large Language Models and Agentic AI Systesm (2026)
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Model Autophagy: Quantifying Epistemic Decay and Governance Intervention in Recursive AI Training Ecosystems (2026)
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Model Autophagy: Quantifying Epistemic Decay and Governance Intervention in Recursive AI Training Ecosystems (2026)
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AI Behavioral Assurance: A Lean Six Sigma Methodology for the LIfecycle Governance of Large Language Models and Agentic AI Systesm (2026)
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Adaptive AI Governance as a Lifecycle Control System: From Static Compliance to Continuous Oversight (2026)
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Adaptive AI Governance as a Lifecycle Control System: From Static Compliance to Continuous Oversight (2026)
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Echo Chamber Dynamics in LLMs: Mitigating Bias and Model Drift (2026)
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Cybersecurity Risks in the Deployment and Use of Digital Business Cards: Implications for Organizations and End-Users (2023)
Collaboration Network
Top Collaborators
- Cybersecurity Risks in the Deployment and Use of Digital Business Cards: Implications for Organizations and End-Users
- Echo Chamber Dynamics in LLMs: Mitigating Bias and Model Drift
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