AI Labeling Platform with Validation Observations for Secure Data
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Solution Overview
Problem
Traditional labeling platforms for AI systems lack mechanisms to enforce security protocols for sensitive data, such as health records, and often result in ambiguous labels that are discarded, especially when dealing with regulatory constraints like HIPAA-protected data.
Innovation Solution
A labeling platform that verifies the authorization of labeling entities to access sensitive data and includes a validation observation type to confirm the accuracy of labels, allowing edits to correct inaccuracies, thereby ensuring proper labeling and reducing discarded data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional labeling platforms are used for sensitive data, then labeling process is simple, but security protocols cannot be enforced and data may be exposed to unauthorized entities
Solution Approach 1:
The patent introduces an intermediary authorization verification mechanism that acts as a mediator between labeling entities and sensitive data. The platform verifies authorization credentials of labeling entities before allowing access to records, ensuring security protocols are enforced without requiring complex changes to the overall labeling platform architecture.
2Manufacturing precision
If labeling entities create labels without validation, then labeling process is fast, but ambiguous labels are created that may be discarded
Solution Approach 1:
The patent implements preliminary validation action by introducing a validation observation type that allows labeling entities to confirm the accuracy of their labels before final submission. This preliminary check prevents ambiguous labels from being discarded later, improving label accuracy without significantly impacting labeling speed.
Solution Approach 2:
The patent incorporates feedback mechanisms where validation results are returned to labeling entities. When a label is validated, confirmation feedback is provided; when invalid, correction feedback is given, allowing labeling entities to improve their labeling accuracy through iterative feedback loops.
3Reliability
If all records are labeled equally, then labeling is straightforward, but important sensitive records may not receive adequate attention
Solution Approach 1:
The patent applies local quality by assigning different priority levels to different records based on their sensitivity and importance characteristics. Instead of treating all records uniformly, the system identifies and prioritizes specific records that require more attention, such as those containing highly sensitive information or critical for AI training accuracy.
Data Source
AI summary
A method includes identifying records in a database for labeling; presenting one of the records in the database to a first labeling entity; and receiving a first observation on an information source in the one of the records from the first labeling entity. The first observation has one of a plurality of observation types associated therewith. The plurality of observation types including a validation observation type in which the first observation comprises a confirmation of whether a second observation on the information source in the one of the records from another labeling entity is accurate and an edit for the second observation when the second observation is confirmed as inaccurate. The one of the records is updated in the database with the first observation on the information source in the one of the records from the first labeling entity.