AI Content Provenance Tracking for Authorship Accountability
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Solution Overview
Problem
Existing computer systems struggle to manage content generated by artificial intelligence (AI) effectively, particularly in legal document generation, source code versioning, and collaborative document editing, due to challenges such as hallucinations, jurisdictional discrepancies, lack of version control, and inability to track AI contributions, leading to inconsistencies and legal and intellectual property issues.
Innovation Solution
Implement mechanisms for data provenance tracking that provide detailed metadata about AI-generated content, including authorship, history of changes, and context, using tools like document validators, AI-authorship detectors, and trust estimators, integrated into document management systems to ensure transparency and accountability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-generated content is integrated into document management systems, then productivity and content generation capability are improved, but provenance tracking and accountability become difficult to maintain
Solution Approach 1:
The patent introduces a provenance tracker as an intermediary component that mediates between AI generation processes and document management systems. This tracker captures metadata about AI-generated content, including the AI model used, prompts, and generation parameters, and stores it alongside the document content. This resolves the contradiction by maintaining provenance information without interfering with the productivity benefits of AI integration.
Solution Approach 2:
The patent segments document content into AI-generated portions and human-authored portions, tracking them separately through provenance metadata. This segmentation allows the system to maintain detailed provenance records for AI-generated content while preserving the overall document management functionality, thus resolving the contradiction between productivity improvement and information loss.
2Reliability
If detailed provenance tracking is implemented for AI content, then accountability and transparency are improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by capturing provenance metadata at the point of AI content generation, before the content is integrated into the document management system. The provenance tracker records information about the AI model, prompts, and generation parameters immediately during the generation process. This approach ensures accountability is established early without requiring complex tracking mechanisms to be implemented throughout the entire document lifecycle.
Solution Approach 2:
The patent uses copying by creating a provenance record that copies relevant metadata about AI-generated content and stores it alongside the actual content in the document management system. This copied provenance information can then be accessed and displayed without requiring complex real-time tracking mechanisms, thus improving accountability while managing system complexity.
3Productivity
If AI tools are used for document generation, then productivity is improved, but hallucinations and accuracy issues increase
Solution Approach 1:
The patent implements feedback mechanisms that allow users to review AI-generated content and correct hallucinations or inaccuracies. The system tracks these corrections and their provenance, enabling continuous improvement. This feedback loop maintains high productivity by utilizing AI's rapid generation capability while addressing accuracy issues through user verification and correction, with the provenance tracker recording all feedback and corrections made.
Data Source
AI summary
This application concerns software-based improvements to computer systems. It relates to an apparatus, method, or program that allows computer systems to manage content generated with artificial intelligence by providing mechanisms for representing and reasoning about the provenance of said content. The application discloses several embodiments in different practical contexts, including change tracking for legal document generation, version control for source code, and real-time collaborative document editing.