Content Editing Software With Auditable Human–AI Authorship Tokens
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Solution Overview
Problem
Traditional content editing software lacks the capability to accurately distinguish between human-authored and artificially-authored content, particularly in cases of mixed authorship, leading to inefficiencies in training data quality, security, and compliance issues.
Innovation Solution
Implementing authorship tokens within content editing software to automatically identify and label regions of content as being authored by humans or artificial intelligence, using methods such as detecting edits through human interface devices and applying tokens to content during editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content editing software is used without authorship tracking, then the software is simple and easy to operate, but it cannot distinguish between human-authored and artificially-authored content leading to data quality and compliance issues
Solution Approach 1:
The patent introduces authorship tokens as intermediary elements that mediate between the content editing functionality and the authorship attribution requirement. These tokens are automatically generated and attached to content regions, serving as a bridge that enables precise authorship tracking without fundamentally altering the core editing software architecture.
Solution Approach 2:
The patent segments content into distinct regions with associated authorship tokens, allowing individual tracking of human-authored versus artificially-generated portions. This segmentation enables precise measurement of authorship attribution while maintaining the overall structure of the content editing system.
2Reliability
If authorship tokens are implemented to track content provenance, then training data quality and security are improved, but the device complexity and operational overhead increase
Solution Approach 1:
The authorship token system operates autonomously, automatically generating and attaching tokens to content regions without requiring manual intervention from users. The system self-manages the tracking of human versus artificial authorship, reducing operational overhead while maintaining reliable provenance records.
Solution Approach 2:
Authorship tokens are generated and attached to content regions in advance, before any compliance review or data quality assessment is needed. This preliminary action ensures that provenance information is already available when required, eliminating the need for retroactive analysis and simplifying operations.
3Loss of information
If manual tracking of authorship is attempted, then some provenance information can be captured, but it is time-consuming and error-prone reducing productivity
Solution Approach 1:
The patent replaces manual, mechanical tracking methods with an automated computational system that uses algorithms to detect and attribute authorship. This substitution eliminates human error and time-consuming manual processes while maintaining complete provenance information, thereby preserving content creation efficiency.
Solution Approach 2:
The system continuously monitors content creation activities and provides real-time feedback by attaching authorship tokens as content is generated. This immediate feedback loop ensures complete provenance capture without interrupting the content creation flow, maintaining high productivity while preventing information loss.
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.