AI Evaluation Framework Using Temporary Labels
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Solution Overview
Problem
Existing AI systems face challenges in evaluating their performance on datasets without ground-truth labels, particularly due to distribution shift, which can lead to biased predictions and unreliable outcomes.
Innovation Solution
A framework is provided that assigns temporary labels to data points in working datasets, uses these labeled data to train distinct models, and evaluates their performance to determine the most accurate temporary labels, thereby assessing AI system performance without ground-truth labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground-truth labels are used for evaluation, then measurement precision is improved, but loss of information increases due to inability to evaluate real-world performance
Solution Approach 1:
The patent introduces an intermediary evaluation framework that uses temporary labels and multiple distinct models as mediators to bridge the gap between ground-truth labeled data and unlabeled real-world data. These intermediaries enable performance assessment on unlabeled data by comparing predictions across multiple models, allowing evaluation in real-world scenarios without direct access to ground truth.
Solution Approach 2:
The system implements feedback mechanisms where predictions from multiple distinct models are compared and evaluated against each other. The framework uses the agreement/disagreement among models as feedback to assess confidence and identify potential biases, enabling continuous improvement and retraining of models based on evaluation results even without ground-truth labels.
2Adaptability or versatility
If AI systems are deployed on data in the wild, then adaptability is improved, but reliability deteriorates due to distribution shift and lack of ground-truth labels
Solution Approach 1:
The patent applies preliminary action by training multiple distinct models on labeled training data before deployment. These models are pre-evaluated and their predictions are stored as reference points. When deployed on unlabeled real-world data, the system can immediately compare predictions against these pre-trained models to assess reliability without requiring ground-truth labels at deployment time.
Solution Approach 2:
The framework changes parameters by using temporary labels and adjusting the confidence thresholds based on the agreement among multiple models. The system dynamically modifies evaluation parameters such as prediction confidence levels and bias assessment metrics based on the distribution characteristics of the unlabeled data, allowing reliable operation despite distribution shift.
3Productivity
If temporary labels are assigned to evaluate performance, then productivity is improved by enabling evaluation without ground truth, but measurement precision may deteriorate
Solution Approach 1:
The patent merges predictions from multiple distinct models to compensate for the imprecision of temporary labels. By combining and comparing predictions across multiple models, the framework achieves more reliable performance assessment than any single model could provide alone. The merging of multiple perspectives offsets the inherent uncertainty in temporary labeling.
Solution Approach 2:
The system performs partial action by using temporary labels only for the portion of evaluation that can be done without ground truth, while using ground-truth labeled data for training and initial validation. This partial approach to temporary labeling, combined with selective use of ground truth where available, maintains acceptable measurement precision while enabling overall productivity gains.
Data Source
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AI summary
Disclosed systems and methods provide a framework for evaluating AI systems without ground-truth annotations. The disclosed embodiments may assign temporary labels to data points in sets of working data and use the temporarily labeled data to train one or more distinct models. These models may be evaluated to determine which has the highest performance and is thus indicative of the temporary labels most likely to be correct.