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Enterprise AI Analysis: Sustainability as a guiding principle for copyright reform: regulating the use of generative Al in the field of research and education

Enterprise AI Analysis

Sustainability as a guiding principle for copyright reform: regulating the use of generative Al in the field of research and education

This article analyzes the complex relationship between Generative Al (Gen Al), sustainability, and copyright, focusing on the use of copyrighted materials for research and education using a combined theoretical and conceptual methodology. On one hand, Gen Al can be transformative by enabling broader access to content, for example, by overcoming language barriers, compiling information in very large datasets, and enabling customized educational experiences that strengthen incentives to learn, thus helping to level the playing field in research and education. These functions contribute to fulfilling the right to education as provided by the Sustainable Development Goals (SDGs) of the United Nations, specifically, SDG4, and the right to research, as embodied in the human rights framework. On the other hand, Gen Al output poses risks in terms of false or lack of attribution to creators, lack of scientific integrity and manipulation of works, and prejudicing the moral interests of authors, which form part of the human rights framework. This article offers preliminary suggestions on how sustainability can be used as a guiding principle to find the right balance between facilitated access to knowledge for education and research and compliance with the moral rights of authors when dealing with Gen Al. The two pillars of this approach are (1) the set-up of a transparent and "human-centric" copyright framework regulating Gen Al and (2) an appropriate governance structure that can easily provide relief in case of moral rights violations prejudicial to research and education, as well as ethical innovation more broadly.

Executive Impact: Key AI-Driven Outcomes

Leveraging Generative AI with a sustainable and ethical copyright framework can drive significant improvements across educational and research enterprises.

0% Enhanced Content Accessibility
0% Improved Learning Efficiency
0% Reduction in Attribution Issues
0% Acceleration of Ethical Innovation

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Copyright Law
Artificial Intelligence
Sustainability
Education & Research Ethics

Copyright Law Implications

The article highlights the tension between facilitating access to knowledge via Gen AI and protecting authors' moral rights, particularly concerning attribution and integrity. It proposes a 'human-centric' copyright framework with transparent training data and effective redress mechanisms for violations.

Artificial Intelligence Dynamics

Generative AI offers transformative potential for education and research, enabling broader access and personalized learning. However, it poses significant risks such as false attribution, lack of scientific integrity, and manipulation of works, necessitating robust governance and ethical guidelines.

Sustainability Framework

Sustainability, embodied in SDG4 (quality education) and human rights, serves as a guiding principle for copyright reform. The framework should balance knowledge access with moral rights protection to ensure equitable, ethical, and long-term benefits for society.

Education & Research Ethics

Ethical considerations in Gen AI use for education and research are paramount, particularly regarding transparency of sources, scientific integrity, and preventing biased information. A human-centric approach is crucial to protect learners and researchers from misinformation and ensure proper attribution.

SDG4 Core Goal: Quality Education & Research Right

Proposed GenAI Copyright Regulation Flow

Author identifies moral rights violation
Notifies Regulation Authority (AI Office)
Authority requests info from AI provider
Compliance measures enforced (e.g., content removal)
Resolution & Ethical Innovation

Current vs. Sustainable GenAI Copyright

Aspect Current State (Risks) Proposed Sustainable Framework (Benefits)
Attribution
  • Lack of recognition
  • False association
  • Transparent sourcing
  • Clear author identity
Content Integrity
  • Decontextualization
  • Manipulation of works
  • Safeguarded moral rights
  • Scientific accuracy
Access to Knowledge
  • Potential for biased info
  • Disinformation risks
  • Broader, equitable access
  • Verified reliable sources
Governance
  • Fragmented, unclear responsibilities
  • AI Office as central regulator
  • Human-centric approach

Case Study: The PhD Student Dilemma

A PhD student relies on Gen AI for research and finds untraceable sources and inadequate scientific information, casting doubt on their work's credibility. This highlights the critical need for transparent attribution and verifiable information in Gen AI outputs for academic integrity. The proposed framework aims to prevent such scenarios by mandating clear source disclosure and robust moral rights protection, ensuring research outputs are trustworthy and ethical.

Conclusion: Robust attribution and integrity mechanisms are vital for academic use of Gen AI.

Advanced ROI Calculator: Quantify Your AI Impact

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Your Roadmap to Ethical & Sustainable AI Integration

Our proven phased approach ensures a smooth, secure, and human-centric adoption of Generative AI within your enterprise, respecting copyright and promoting innovation.

Phase 1: Ethical AI Strategy & Assessment

Comprehensive audit of current data practices, identification of high-impact AI opportunities, and development of a tailored human-centric AI strategy aligned with sustainability goals and copyright compliance.

Phase 2: Secure & Transparent Data Foundation

Establishment of secure data pipelines, implementation of robust attribution and transparency mechanisms for AI training data, ensuring compliance with moral rights and data privacy regulations.

Phase 3: Pilot Deployment & Governance Framework

Deployment of pilot AI solutions with integrated ethical monitoring, establishment of an internal AI governance structure, and training for employees on responsible AI use and moral rights adherence.

Phase 4: Scaling & Continuous Compliance

Full-scale AI integration across relevant departments, ongoing performance monitoring, and adaptive refinement of the copyright framework and ethical guidelines to ensure long-term sustainability and innovation.

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