Adaptive Correction Mechanism for AI Prediction Alignment
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Solution Overview
Problem
Conventional data-driven artificial intelligence models often conflict with experts' intuition due to differing correlations and trends, particularly when training data is limited or costly to diversify, leading to inefficiencies in applications where human insight is crucial.
Innovation Solution
A method that estimates the similarity of new data inputs to training data, allowing for adaptive correction of predicted class labels to align with experts' intuition, using a framework that combines machine learning models with intuition-based corrections while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data-driven AI models are used for prediction, then prediction speed and automation are improved, but prediction accuracy conflicts with experts' intuition especially when training data is limited
Solution Approach 1:
The patent introduces an intermediary mechanism that compares AI model predictions with expert intuition and selectively applies corrections. The system uses a correction module that acts as a mediator between the data-driven AI model and expert knowledge, applying intuition-based corrections when the AI model's predictions diverge from expert expectations, thereby resolving the contradiction between automation speed and prediction accuracy.
Solution Approach 2:
The patent applies local quality by selectively correcting predictions only in specific cases where expert intuition diverges from AI model outputs. Rather than uniformly applying expert rules across all predictions, the system identifies and corrects only those local instances where the AI model's prediction conflicts with expert intuition, maintaining efficiency while improving accuracy where needed.
2Measurement precision
If training data is diversified to improve model performance, then prediction accuracy is improved, but data collection cost and time increase
Solution Approach 1:
The patent uses expert intuition as an intermediary to compensate for limited training data. Instead of requiring extensive diversified training data to achieve good performance, the system incorporates expert knowledge as a corrective mechanism that bridges the gap between limited data and desired model performance, thereby improving accuracy without proportionally increasing data collection efforts.
Solution Approach 2:
The patent applies preliminary action by pre-incorporating expert intuition into the prediction system. Rather than collecting and processing extensive training data to capture expert knowledge, the system预先 integrates expert rules and intuition that can be applied directly to correct AI predictions, reducing the need for extensive data collection while maintaining model performance.
3Measurement precision
If expert intuition is fully integrated into predictions, then prediction accuracy aligns with human expertise, but automation level and processing speed decrease
Solution Approach 1:
The patent applies local quality by selectively applying expert intuition corrections only where needed. The system maintains high automation by using AI models for the majority of predictions, while locally applying expert-based corrections only in specific cases where the AI prediction conflicts with expert expectations. This selective approach preserves automation levels while improving alignment with expert intuition where necessary.
Solution Approach 2:
The patent uses partial action by applying expert intuition corrections only partially rather than fully. Instead of completely replacing AI predictions with expert rules, the system applies corrections selectively and partially, maintaining the automated AI-driven approach while incorporating expert knowledge to the extent necessary to resolve conflicts, thereby balancing automation and expert alignment.
Data Source
AI summary
One embodiment provides a method comprising receiving training data and experts' intuition, training a machine learning model based on the training data, predicting a class label for a new data input based on the machine learning model, estimating a degree of similarity of a target attribute of the new data input relative to the training data, and selectively applying a correction to the class label for the new data input based on the degree of similarity prior to providing the class label as an output. The target attribute is an attribute related to the experts' intuition.