Dale Rutherford
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
PhD
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Dale Rutherford's research investigates the governance and behavioral assurance of artificial intelligence systems, particularly large language models (LLMs). His work addresses the risks associated with LLM deployment, including potential knowledge narrowing and epistemic decay in recursive training ecosystems. Rutherford also examines cybersecurity implications in the adoption of new technologies, such as digital business cards, for organizations and end-users.
His recent publications focus on quantifying and managing AI governance challenges throughout the lifecycle of LLMs and agentic AI systems. He has explored methodologies inspired by Lean Six Sigma for ensuring AI behavioral assurance and has investigated the phenomenon of 'model autophagy,' which describes epistemic decay in AI training environments. Rutherford's scholarship metrics indicate an h-index of 1, with 14 total publications and 2 citations.
Metrics
- h-index: 1
- Publications: 14
- Citations: 2
Positions
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PhD 2025–presentThe Center for Ethical AI Publications ORCID
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PhD publications 2023–2026University of Arkansas at Little Rock ORCID
Selected Publications
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Interaction-induced knowledge narrowing risk in LLM systems (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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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
- Echo Chamber Dynamics in LLMs: Mitigating Bias and Model Drift
- Interaction-induced knowledge narrowing risk in LLM systems
- Cybersecurity Risks in the Deployment and Use of Digital Business Cards: Implications for Organizations and End-Users
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