THIS ARTICLE IS PART OF OUR FEATURE ON GENERATIVE AI
Understanding generative mechanisms to assess risk
To properly grasp the issues surrounding plagiarism, we need to understand the fundamental process of generative AI. Unlike a database, which retrieves and recomposes existing fragments, AI learns linguistic patterns and generates content by probabilistic word-by-word prediction.
The ChatGPT/Claude/Gemini case: safeguards integrated
Leading platforms implement sophisticated plagiarism prevention mechanisms:
Statistical analysis of outputs: Tests conducted on billions of generated words show plagiarism rates of less than 0.02%, statistically negligible. The probability of two users obtaining identical output, even with similar prompts, is virtually nil.
Automatic detection and filtering: The systems incorporate detection algorithms that prevent the reproduction of copyrighted passages or identifiable proprietary content.
Risk mitigation strategies for companies
- Systematic verification protocols for professional services:
Implement a 3-step validation workflow:
- AI generation with specialized prompts
- Automated plagiarism scanning using tools such as Copyscape or Grammarly
- Expert review for customization and validation
- Documentation of sources and processes
Maintain clear traceability:
- Prompts used and context of generation
- Integrated proprietary data sources
- Modifications and enhancements made
- Validation by internal experts
- Development of distinctive content
Originality comes from your unique approach:
Example - Transformation consultancy
|
Generic approach:
"Digital transformation requires organizational cultural change."
Distinctive approach:
"Our ADAPT™ (Assess-Design-Activate-Pilot-Transform) methodology developed over 200+ projects shows that 73% of transformation failures result from underestimating middle-management resistance. We specifically address this critical organizational layer right from the assessment phase."
|
Rights and responsibilities management
Legal clarification for commercial use:
Most generative AI platforms grant commercial usage rights to generated content, but with important nuances:
- Generated outputs: Generally free for commercial use
- Proprietary Prompts: Remain your intellectual property
- Training data: Integrated but not directly reproducible
- Enhancements: Your additions remain entirely proprietary
Legal recommendation: For companies handling sensitive information, consult an intellectual property attorney to define clear guidelines for the use of generative AI.
Ethics and transparency: building trust
Context-appropriate disclosure:
B2B Content Marketing: No systematic disclosure necessary if the content is enriched by your expertise and meets audience needs.
Research and studies: Mention AI assistance for methodological transparency.
Sensitive or regulated content: Disclosure recommended as a precaution.
Ethical structure for professional use:
- Added value: AI should amplify your expertise, not replace it.
- Internal transparency: Your teams understand when and how AI is used.
- Maintained quality: Quality standards remain the same or higher
- Assumed responsibility: You remain responsible for the accuracy and relevance of the final content