Future Internet Systems
Impact of Generative AI on Author's Metrics and Copyright Ownership: Digital Labour, Ethical Attribution, and Traceability Frameworks for Future Internet Systems
This research analyzes how Generative AI (GAI) impacts author metrics and copyright, proposing the Chujoyi-TraceNet framework for ethical attribution and traceability. It demonstrates that current GAI systems often obscure human creativity and intellectual labor, leading to an 'invisible footprint' where original content is used without measurable engagement or attribution. Chujoyi-TraceNet, integrating real-time tracking, blockchain licensing, and metadata watermarking, aims to re-center ethics and recognition in AI-mediated knowledge ecosystems, fostering a more transparent and equitable digital economy.
Executive Impact: Quantifying AI's Value
Our analysis reveals the profound implications and potential solutions for ethical AI integration, demonstrating tangible benefits for creators and enterprises.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Ethical Implications
Invisible Footprint Phenomenon
Chujoyi-TraceNet Framework
Technical Components
Monetisation Opportunities
Generative AI systems, while advancing content creation, obscure human intellectual labor. The research highlights critical issues such as bias, misinformation, and lack of transparency, especially concerning how LLMs use and redistribute human-created content without traceable attribution. This creates a structural disconnect between knowledge production and recognition, raising concerns about intellectual ownership, economic sustainability, and accountability in AI-mediated ecosystems.
A key finding from the case study is the 'no-footprint' condition, where leading LLMs accessed and processed content from a digital creative platform (apoetsbrain.wordpress.com) without generating any measurable analytics signals (page views, referrals, or visitor locations). This invisibility undermines traditional metrics for content creators, eroding professional credibility and raising ethical and legal concerns about intellectual property and fair use.
The proposed Chujoyi-TraceNet (CTN) is a socio-technical framework designed to address the ethical and operational deficiencies in current LLM architectures. It integrates real-time content interaction tracking, dynamic licensing agreements, geospatial usage mapping, and attribution metadata. CTN aims to restore visibility, agency, and recognition to human contributors by ensuring equitable attribution and compensation, thereby fostering more democratic and accountable learning media.
CTN includes Unique Content Hashing for source identification (SHA-256), a Probabilistic Attribution Model to quantify source contributions (P(Sj|Ok)), a Traceability Score (T(Ok)) based on Shannon entropy for reliability, and a Compensation Model for revenue sharing. It also integrates AI-Watermarking (Fourier transforms) and statistical fingerprinting (TF-IDF) for web monitoring, ensuring content provenance and ethical AI usage.
CTN systems can open new revenue streams through AI consumption-based royalties via smart contracts, subscription-based AI access models for curated datasets, and attribution fees for AI-generated derivative content. This ensures original creators are compensated proportionally, transforming AI-driven content consumption into financially sustainable practices.
Invisible Labour
The core problem identified: human creative work used by AI without detectable traces.
Enterprise Process Flow
Content Ingestion & Hashing
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AI Interaction & Synthesis
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Probabilistic Attribution
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Traceability Scoring
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Attribution-Linked Compensation
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ScholarNet Publishing Platform: Restoring Author Recognition
ScholarNet, a digital repository, integrated CTN to address AI paraphrasing without citation. Each article now receives a cryptographic watermark (Stylometric Analysis + Lexical Hashing). When AI generates similar content, a probability score (P(Ai|Gk)) attributes it to the original article, ensuring automated author recognition and enforcing ethical usage. This transforms previously uncredited usage into a traceable and compensable interaction.
Advanced ROI Calculator
Estimate the potential annual savings and reclaimed human hours by implementing ethical AI attribution and traceability in your organization.
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Implementation Roadmap
A phased approach to integrating Chujoyi-TraceNet into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Pilot & Integration (0-6 Months)
Implement Chujoyi-TraceNet with a select group of content creators and AI models. Focus on refining content hashing, watermarking, and basic attribution logic. Integrate with existing web analytics tools for initial data collection.
Phase 2: Scale & Compensation Model (6-18 Months)
Expand CTN to a wider user base. Develop and test the probabilistic attribution and compensation models, including blockchain-enabled licensing and micropayments. Establish regulatory compliance dashboards for monitoring AI interactions.
Phase 3: Ecosystem-Wide Adoption (18-36 Months)
Promote CTN as an industry standard. Foster partnerships with major AI developers, publishers, and regulatory bodies for widespread adoption. Explore new monetisation opportunities and develop advanced features for multimodal content and real-time attribution.
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