Subgroup-Aware Loss Function for ML Performance Equity
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Solution Overview
Problem
Machine learning models trained on unbalanced datasets suffer from performance degradation, leading to mismatches between user attributes and model predictions, especially for less frequently observed subgroups.
Innovation Solution
Modifying the loss function by adding terms such as error regularization, divergence minimization, and HSIC regularization to reduce performance variations across different user subgroups, ensuring more accurate and consistent predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained on unbalanced datasets, then the model can be trained efficiently with available data, but the model performance degrades for less frequently observed subgroups
Solution Approach 1:
The loss function is modified to apply different weighting to different user subgroups. Specifically, the patent introduces a subgroup-aware loss function that assigns higher weights to underrepresented subgroups and lower weights to overrepresented subgroups, thereby locally adjusting the training objective to improve performance for specific subgroups while maintaining overall training efficiency
Solution Approach 2:
The patent changes the parameters of the loss function by introducing subgroup-specific weighting factors and variance regularization terms. These parameter modifications transform the standard loss function into a subgroup-balanced loss function that penalizes performance variations across subgroups, thereby improving model reliability for less frequently observed subgroups without sacrificing training productivity
2Ease of operation
If standard loss functions are used for training, then the training process is simple and fast, but prediction errors vary significantly across different user subgroups
Solution Approach 1:
The patent performs preliminary action by modifying the loss function before the training process begins. The subgroup-aware loss function is designed in advance to incorporate subgroup balancing objectives, allowing the model to learn equitable performance across subgroups from the start rather than requiring post-training adjustments or complex during-training interventions
Solution Approach 2:
The loss function is segmented into multiple components: the standard prediction error term and additional subgroup-balancing terms. This segmentation allows the training process to simultaneously optimize for overall accuracy and subgroup performance consistency, maintaining ease of operation while improving measurement precision across different user subgroups
3Measurement precision
If model predictions are optimized for overall accuracy, then the average performance improves, but performance disparities across subgroups increase
Solution Approach 1:
The patent merges the standard accuracy optimization objective with the subgroup performance balancing objective into a unified loss function. By combining these two objectives with appropriate weighting, the model achieves both high average prediction accuracy and uniform performance distribution across subgroups, resolving the contradiction between overall accuracy and performance equity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reducing the difference in performance of a model across groups and sub-groups within the same group of users with similar characteristics for providing digital components. Methods can include identifying, a loss function that generates a loss representing a measure of performance the model seeks to optimize during training. The loss function is modified by adding an additional term to the loss function. The model is trained using the modified loss function. A request for digital component is received that includes a user group identifier. The model generates one or more user characteristics based on which one or more digital components are selected and transmitted to the client device of the user.