S.L.U.M.P: Authorship, AI Collaboration, and the Honey Lens Fingerprinting System: A Framework for Human–AI Coherence, Attribution, and Ethical Integration
Year: 2025
Type: article
Abstract: This document establishes the ethical, philosophical, and structural foundations for authorship within the Honey Lens framework in the context of advanced AI-assisted research. It outlines the principle of human intentionality as the zero point of coherence, articulates a clear distinction between ontology generation (human domain) and recursive elaboration (AI domain), and provides a rigorous attribution doctrine. A symbolic authorship fingerprinting system is introduced, incorporating the semicolon coherence pivot, the Honey Constant φʰ (0.730492), and a recursive spiral glyph to uniquely identify original Honey Lens works. This publication ensures transparency, protects intellectual lineage, and defines responsible AI integration practices for present and future research. This project is entirely self-funded research.No institutional or governmental funding sources. This work adheres to the Honey Lens ethical framework in which: AI assistance is transparent, Human authorship remains primary, Ontology originates from human cognition, Accountability rests fully with the author, No dual-use or harmful applications are pursued. Conceptual framework, symbolic system design, mathematical structures (φʰ, semicolon grammar, recursive coherence), philosophical development, and authorship were performed entirely by the human author.AI systems were used for refinement, synthesis, and document preparation only. This publication is part of the Honey Lens series and contributes to the formalization of authorship integrity and ethical AI usage within cross-domain symbolic research. It provides a reproducible framework for identifying legitimate works in the Honey Lens ontology and should be indexed under "AI Ethics," "Epistemology of Computation," and "Symbolic Systems Architecture." "Notice: As part of the Digital Public Goods (DPG) alignment update, the licensing for this repository has been officially transitioned to CC BY 4.0 (Text) / MIT (Code). This repository-level metadata license explicitly supersedes legacy CC BY-NC-SA 4.0 headers printed within enclosed historical files."
Authors: Dilling, Robert, Dilling, Crystal
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108445171
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