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Doctoral dissertation · Public PDF
Governing BME Amplification in Large Language Models
A Standards-Aligned Framework for LLM Data Lifecycle Risk Detection and Ethical AI Governance
Author
Dale A. RutherfordPublished
2026Source
University of Arkansas at Little Rock
Abstract / overview
This dissertation develops a standards-aligned framework for detecting and mitigating bias, misinformation, and error across the large language model lifecycle. It introduces the BME Metric Suite and connects behavioral measurement, information-quality monitoring, and governance interventions through the ALAGF, MIDCOT, and SymPrompt frameworks.
Keywords
LLM lifecycle · Information quality · Behavioral measurement · AI governance
Research focus
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Measurement — BME measures support examination of bias, misinformation, and error.
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Lifecycle — Links information-quality monitoring with governance interventions.
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Standards — Connects the framework with recognized AI governance standards.
Full text access
Public document
Consult the posted dissertation for its copyright and reuse terms.
Suggested citation
Rutherford, D. A. (2026). Governing BME amplification in large language models: A standards-aligned framework for LLM data lifecycle risk detection and ethical AI governance [Doctoral dissertation, University of Arkansas at Little Rock]. https://doi.org/10.5281/zenodo.23884500
Publication details
Document type
Doctoral dissertationAuthor / source
Dale A. RutherfordPublication date
2026Source / venue
University of Arkansas at Little RockStatus
Doctoral dissertation; see the deposited document for institutional and degree details.Format & length
PDF · 313 pagesIdentifier
10.5281/zenodo.23884500Access
Public PDF; other formats and editable originals by requestLicense / terms
Consult the posted dissertation for its copyright and reuse terms.
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