Gradient Boosting Fairness via Constrained Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional gradient boosting machine learning algorithms face challenges in algorithmic fairness, as they often display bias and discriminatory decision-making across sub-groups, with existing fairness methods being either inapplicable, resource-intensive, or requiring sensitive attributes at inference time.
Innovation Solution
A constrained optimization method for gradient boosting machines is introduced, which configures a sequential error-based additive machine learning model to minimize a loss function with fairness constraints based on group disparities, using proxy metrics and Lagrange multipliers to efficiently optimize while maintaining predictive performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional GBDT algorithms are used, then predictive performance is achieved, but algorithmic bias and discriminatory decision-making occur across sub-groups
Solution Approach 1:
The patent introduces fairness constraints as intermediary elements that mediate between the model's predictive performance and its fairness properties. These constraints act as mediators that guide the optimization process to achieve both accuracy and fairness simultaneously, rather than treating fairness as a post-processing step or sacrificing performance entirely.
Solution Approach 2:
The patent modifies the optimization parameters by incorporating fairness constraints into the loss function. By changing the parameter space to include fairness metrics (such as demographic parity, equalized odds, or predictive equality constraints), the model optimization process directly learns fair decision boundaries while maintaining predictive performance.
2Object-affected harmful factors
If existing fairness methods are applied to GBDT, then fairness improvement is achieved, but significant train time overhead is incurred
Solution Approach 1:
The patent merges the fairness optimization objective with the standard GBDT training process by integrating fairness constraints directly into the loss function. This combination allows the model to optimize for both predictive performance and fairness simultaneously in a single training run, rather than requiring separate fairness adjustment steps that would increase training time.
Solution Approach 2:
The patent creates a universal optimization framework that handles both predictive performance and multiple fairness constraints through a single unified loss function. This multi-functional approach allows the same training process to achieve accuracy, demographic parity, equalized odds, or other fairness metrics depending on which constraints are activated, eliminating the need for multiple separate training runs.
3Object-affected harmful factors
If fairness constraints are added to the loss function, then fairness is improved, but computing resources and memory usage increase
Solution Approach 1:
The patent implements partial fairness enforcement by allowing configurable relaxation of fairness constraints through hyperparameters. Users can specify the degree of fairness enforcement (e.g., through constraint weights or tolerance parameters), enabling a trade-off between fairness improvement and computational cost. This partial action approach allows the system to achieve sufficient fairness without the full computational overhead of strict constraint enforcement.
4Object-affected harmful factors
If fairness constraints based on group disparities are implemented, then discriminatory decision-making is reduced, but model complexity increases
Solution Approach 1:
The patent applies local quality by implementing fairness constraints that operate locally on specific protected groups rather than requiring global model restructuring. Each fairness constraint (e.g., demographic parity for a specific group, equalized odds for a specific group) can be independently configured and enforced, allowing fairness improvements for particular groups without increasing overall model complexity across the entire system.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The present disclosure relates to a constrained optimization computer-implemented method for gradient boosting machines. In various embodiments, a process for constrained optimization for sequential error-based additive machine learning models (e.g., gradient boosting machines) includes configuring a sequential error-based additive machine learning model, receiving training data, and using one or more hardware processors to train the sequential error-based additive machine learning model using the received training data. The training includes performing optimization iterations to minimize a loss function that includes a fairness constraint, where the fairness constraint is based at least in part on disparities between groups.