Tree-Based Model Branch Pruning for Algorithmic Bias Mitigation
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Solution Overview
Problem
Machine learning models often exhibit biases and disparate impacts on protected classes due to various sources, including training data and algorithmic design, which can lead to unfair treatment and legal issues, and existing debiasing techniques lack fine-grain control and accuracy.
Innovation Solution
A method for debiasing tree-based machine learning models involves pruning specific branches to generate forest models that maintain predictive accuracy while reducing discrimination, using metrics like adverse impact ratio and group separation metrics to identify nodes for removal, ensuring the models are less discriminatory and adhere to minimum accuracy thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional post-processing techniques such as pruning are used for bias mitigation, then the model structure is simplified, but the predictive accuracy deteriorates and the techniques are not parameterizable
Solution Approach 1:
The patent implements dynamic pruning by allowing the pruning threshold and parameters to be adjusted based on fairness metrics and business requirements. The system dynamically determines which branches to prune based on calculated disparity indicators and user-defined fairness constraints, rather than using fixed conventional pruning methods. This enables the model to adapt its structure based on fairness considerations while maintaining acceptable predictive accuracy.
Solution Approach 2:
The patent changes the pruning parameters to be parameterizable based on fairness metrics and business context. Instead of using fixed pruning criteria, the system allows users to define fairness thresholds, disparity indicators, and pruning parameters that can be adjusted according to specific business requirements. This enables fine-grain control over the pruning process and maintains predictive accuracy while achieving fairness goals.
2Object-affected harmful factors
If branches are pruned to reduce discrimination, then the model becomes less discriminatory, but the predictive accuracy may fall below minimum thresholds
Solution Approach 1:
The patent implements feedback mechanisms where the pruning process continuously monitors and adjusts based on fairness metrics and predictive accuracy measurements. The system calculates disparity indicators before and after pruning, and uses this feedback to determine whether additional pruning is needed or if the model should retain certain branches to maintain accuracy thresholds. This iterative feedback loop ensures that the final model meets both fairness and accuracy requirements.
Solution Approach 2:
The patent applies partial pruning by selectively removing only the portions of the model that contribute most to discrimination, rather than removing all branches. The system calculates disparity indicators for different branches and prunes only those exceeding predefined thresholds, while retaining branches that maintain predictive accuracy. This partial action approach allows the model to reduce discrimination to an acceptable level while preserving necessary predictive capabilities.
3Ease of operation
If tree-based models are used for machine learning, then the models provide interpretability and ease of implementation, but they can exhibit biases and disparate impacts that are difficult to detect and mitigate
Solution Approach 1:
The patent segments the tree-based model into individual branches and nodes, allowing systematic analysis of each segment for bias. By breaking down the model structure into manageable units (branches, nodes, leaves), the system can calculate disparity indicators for each segment and identify specific portions that contribute to discrimination. This segmentation makes bias detection more systematic and measurable while maintaining the overall interpretability of tree-based models.
Solution Approach 2:
The patent uses visual and metric indicators to highlight biased branches, analogous to using color changes to indicate problems. The system calculates disparity indicators and fairness metrics that visually stand out when branches exhibit discriminatory patterns. This allows operators to quickly identify and address biased branches while maintaining the overall model structure. The metric calculations provide clear signals about which branches need pruning or adjustment.
Data Source
AI summary
A method is provided for debiasing machine learning models. The method includes obtaining (i) an initial model that is a trained and tree-based machine learning model and (ii) a minimum acceptable threshold accuracy, for (iii) one or more protected classes. The initial model demonstrates adverse impact on one or more protected classes. The method includes identifying branches of the initial model to prune, based on the branches' impact on one or more protected classes. The method includes applying a pruning algorithm to prune the branches of the initial model to generate one or more forest models, such that (i) predictive accuracy of the one or more forest models is above the minimum threshold accuracy, and (ii) the one or more forest models are less discriminatory than the initial mode.