Bias Detection in Predictive Models via Feature Group Analysis
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Solution Overview
Problem
Existing techniques for testing predictive models fail to identify and correct biases, leading to inaccurate predictions due to masked performance metrics that do not reveal biases in specific predictor variables, resulting in ineffective retraining and potential reproduction of errors.
Innovation Solution
A processing device identifies biases by comparing performance metrics and baseline metrics for feature groups in trained predictive models, isolates the biased training data, and generates model corrections to reduce or eliminate biases, thereby improving predictive model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If overall accuracy metric is used to evaluate model performance, then evaluation is simple, but biases in specific predictor variables are masked and not identified
Solution Approach 1:
The patent segments the overall model evaluation into component-level evaluations by analyzing performance metrics for each predictor variable separately. This allows biases in specific variables to be identified without sacrificing the simplicity of automated evaluation, resolving the contradiction between easy evaluation and precise bias detection.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between the model predictions and the evaluation metrics. This intermediary component calculates additional metrics that specifically measure bias in predictor variables, enabling precise bias detection while maintaining the simplicity of the overall evaluation process.
2Loss of time
If model is retrained without identifying bias causes, then retraining process is quick, but errors are reproduced due to unchanged biased training data
Solution Approach 1:
The patent performs preliminary analysis to identify the root causes of biases in the training data before retraining occurs. By detecting and isolating biased data points in advance, the system ensures that retraining actually improves model accuracy rather than simply reproducing existing errors, resolving the contradiction between fast retraining and reliable results.
Solution Approach 2:
The patent implements a feedback mechanism where performance metrics and bias detections from testing feed back into the training data preparation process. This feedback loop allows the system to identify and correct biased data points before retraining, ensuring that time is not wasted reproducing errors and that model accuracy genuinely improves.
3Measurement precision
If detailed performance metrics are generated for each feature group, then biases are detected, but evaluation complexity increases
Solution Approach 1:
The patent applies local quality analysis by generating detailed performance metrics specifically for each feature group where biases are suspected or detected. Rather than uniformly complex evaluation across all aspects, the system focuses detailed analysis where needed, maintaining simplicity elsewhere while achieving high precision in bias detection.
Data Source
AI summary
This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.