AI Prediction Evaluation with Expert Strange-Feeling Labels
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Solution Overview
Problem
Existing AI systems lack the ability to incorporate experts' empirical rules, leading to predictions that deviate from domain experts' expectations, which can be perceived as strange or unexpected.
Innovation Solution
A strange feeling prediction model is developed to evaluate the deviation of AI predictions from experts' empirical rules, generating a strange feeling index to align AI outputs with expert judgment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI prediction models are used to generate predictions, then prediction efficiency and processing speed are improved, but the predictions may deviate from experts' empirical rules and domain knowledge
Solution Approach 1:
The patent implements a feedback mechanism where experts provide labels indicating whether predictions have 'strange feelings' (deviate from empirical rules). These labels are fed back into the system to train a strange feeling prediction model, which continuously refines its ability to align with expert knowledge while maintaining AI prediction efficiency
Solution Approach 2:
The patent introduces an intermediary component - the strange feeling prediction model - that mediates between raw AI predictions and expert knowledge. This intermediary model processes predictions through the lens of domain expertise, translating abstract AI outputs into evaluations of alignment with empirical rules without requiring direct expert intervention in every prediction case
2Reliability
If experts' empirical rules are incorporated into AI predictions, then alignment with domain knowledge is improved, but system complexity increases
Solution Approach 1:
The patent segments the system into distinct functional components: the original AI prediction model, the strange feeling prediction model, and the labeling mechanism. This segmentation allows each component to specialize - the AI model handles prediction generation while the strange feeling model handles knowledge alignment - reducing overall system complexity through modular architecture
Solution Approach 2:
The patent creates a copy of expert knowledge in the form of the strange feeling prediction model. Instead of directly programming experts' empirical rules into the AI system, the system learns and stores a computational representation of expert judgment patterns, allowing flexible application without direct expert involvement in prediction generation
3Measurement precision
If a strange feeling prediction model is trained using expert labels, then prediction accuracy aligned with expert expectations is improved, but data collection and training requirements increase
Solution Approach 1:
The system performs self-service by automatically generating training data through the labeling process. Experts provide labels for a subset of predictions, and the system uses these labeled examples to train the strange feeling prediction model, which then can autonomously evaluate new predictions without requiring continuous expert input, reducing long-term data collection burden
Data Source
AI summary
In the strange feeling prediction device, the prediction result acquisition means inputs diagnosis data to a target prediction model which is a trained prediction model serving as a target, and acquires a prediction result by the target prediction model. The label acquisition means acquires a strange feeling label indicating a strange feeling of an expert with respect to the prediction result. The strange feeling prediction model training means trains a strange feeling prediction model using the prediction result and the strange feeling label. The strange feeling prediction means outputs a strange feeling index indicating the strange feeling with respect to the prediction result outputted by the target prediction model, using the trained strange feeling prediction model.