Automation Classification Model for ML Output Review
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Solution Overview
Problem
Machine learning applications provide confidence scores for their outputs, but these scores do not effectively guide users on whether the outputs require review, leading to inefficient verification processes.
Innovation Solution
An automation classification model is introduced to analyze machine learning outputs, metadata, and calculated values to determine if manual verification is needed, providing a binary indication of whether a review is required or not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidence scores are provided for machine learning outputs, then the reliability of the output indication is improved, but the ease of operation deteriorates because users still cannot determine whether review is needed
Solution Approach 1:
The patent introduces an intermediary component (the review recommendation system) that translates confidence scores into actionable review recommendations. This intermediary processes the confidence score and additional factors to generate a clear indication of whether human review is needed, thereby maintaining reliability while improving ease of operation.
Solution Approach 2:
The system changes the parameter representation from raw confidence scores to review recommendations based on multiple factors including confidence score thresholds, document characteristics, and stakeholder preferences. This parameter transformation makes the output more actionable and easier to interpret while maintaining the underlying reliability information.
2Reliability
If manual review processes are used for all machine learning outputs, then the reliability of results is improved, but the productivity deteriorates due to inefficient verification processes
Solution Approach 1:
The system applies partial action by recommending manual review only for specific outputs that meet certain criteria (low confidence scores, high-stakes classifications, uncertain document characteristics). This selective approach maintains reliability for critical cases while improving productivity by avoiding unnecessary reviews of high-confidence, low-risk outputs.
Solution Approach 2:
The verification process is segmented into automated processing and selective manual review based on the review recommendation. This segmentation allows the system to handle different types of outputs through different pathways, improving overall productivity while maintaining reliability through targeted human review where needed.
3Ease of operation
If an automation classification model is introduced to determine review necessity, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The automation classification model serves multiple functions: it evaluates confidence scores, analyzes document characteristics, considers stakeholder preferences, and generates review recommendations. This multi-functionality consolidates what would otherwise require separate systems into a single unified component, improving ease of operation while managing complexity through functional integration.
Data Source
AI summary
In some embodiments, a first output is received from a first prediction network at a second prediction network. The first prediction network generates the first output from a first input. Also, a second input is received at the second prediction network that describes the first input. The second prediction network analyzes the first output and the second input and generates a second output that classifies the first output in one of a set of classifications. The first output is output with the one of the set of classifications for the second output where the second output indicates whether the first output should be reviewed when the second output is classified in a first classification in the set of classifications or not reviewed when the second output is classified in a second classification in the set of classifications.