Edit Event Tracking With Mock Content for Authorship Attribution
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Solution Overview
Problem
Existing content creation tools struggle to accurately distinguish between human-authored and artificially generated content, particularly in collaborative environments where both contributors are involved, leading to difficulties in copyright attribution, compliance with content policies, and maintaining content quality.
Innovation Solution
The implementation of authorship tokens within content editors to automatically label regions as being authored by humans or artificial intelligence, using edits and other criteria to determine and store authorship information, which can be stored separately or within the content file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content creation tools use generative artificial intelligence to produce content, then productivity and content quality are improved, but the ability to accurately distinguish human-authored from artificially generated content deteriorates
Solution Approach 1:
The system performs preliminary action by embedding authorship tokens in the content at the time of creation, before any analysis or attribution is needed. These tokens are inserted into the content structure during the generation process, allowing future identification of the content's origin without requiring complex analysis later.
Solution Approach 2:
The patent introduces an intermediary mechanism (authorship tokens and metadata) that mediates between the content creation process and the attribution process. These tokens act as a bridge, carrying authorship information through the content without interfering with the content's functionality or quality.
2Measurement precision
If content creation tools automatically label content regions with authorship tokens, then authorship attribution accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the content into distinct regions with different authorship attributes, allowing each portion to be independently labeled. This segmentation enables precise tracking of which parts were created by humans versus AI, without requiring the entire content structure to be fundamentally redesigned.
Solution Approach 2:
The patent uses copying by creating metadata copies of authorship information that parallel the actual content. These copies (tokens, tags, or metadata fields) contain the attribution data without replacing or significantly modifying the original content structure, thus adding minimal complexity.
3Measurement precision
If content creation tools track editing events to determine authorship, then authorship attribution accuracy is improved, but loss of time in processing events increases
Solution Approach 1:
The system performs preliminary action by capturing and storing event data at the moment it occurs during content creation. This real-time capture eliminates the need for retrospective analysis or replay of events, significantly reducing processing time while maintaining accurate attribution.
Solution Approach 2:
The content creation tool itself performs the authorship tracking without requiring external systems or complex post-processing. The tool automatically monitors its own operations, captures relevant events, and generates attribution data as part of its normal functioning, eliminating time-consuming external analysis steps.
Data Source
AI summary
A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.