Fairness-Constrained Machine Learning Training via Iterative Weight Adjustment
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Solution Overview
Problem
Machine learning models often exhibit bias, particularly in critical applications, due to historical disparities and biased training data, leading to unfair treatment of certain groups, and existing methods for addressing fairness are sensitive to hyperparameter settings and may not guarantee the most accurate fair prediction models.
Innovation Solution
A method involving a non-transitory computer-readable medium with instructions to train a fair machine learning model by initializing a bound value and iteratively updating it based on fairness constraints, using a weight vector to weight observation vectors, and adjusting the bound value until no further adjustments are needed, to achieve a fair prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained using historical data, then productivity and cost savings are improved, but bias and unfair treatment of certain groups are introduced
Solution Approach 1:
The patent applies preliminary action by performing fairness-aware reweighting of training observations before the actual model training process. The system calculates importance weights for each training example based on fairness constraints and sensitive attributes, then uses these pre-computed weights to guide the training process. This preliminary preparation ensures that bias mitigation is integrated into the training process from the beginning, allowing the model to learn fair decision boundaries without sacrificing productivity.
Solution Approach 2:
The patent implements parameter changes by modifying the training data distribution through importance sampling weights. The system changes the effective parameters (weights) assigned to different training observations based on their sensitive attributes and fairness constraints. This parameter modification allows the model to adjust its learning process to account for historical biases in the data, thereby reducing unfair treatment while maintaining training efficiency.
2Object-affected harmful factors
If fairness constraints are enforced during model training, then bias is reduced, but model accuracy may deteriorate
Solution Approach 1:
The patent uses parameter changes by adjusting the importance weights of training observations rather than directly constraining the model parameters. This indirect approach allows fairness constraints to be incorporated into the training process without rigidly limiting the model's ability to learn accurate predictions. The weight adjustments guide the model to prioritize fair decision-making while still maintaining predictive accuracy on the target variable.
Solution Approach 2:
The patent applies dynamics by making the training process adaptive through iterative weight computation. The importance weights are not fixed but are dynamically adjusted based on the model's current performance and fairness metrics. This dynamic reweighting allows the system to balance fairness and accuracy iteratively, preventing degradation of model accuracy while enforcing fairness constraints.
3Object-affected harmful factors
If existing fairness methods are applied, then bias mitigation is achieved, but the methods are sensitive to hyperparameter settings and may not guarantee the most accurate fair prediction models
Solution Approach 1:
The patent implements self-service by enabling the training process to automatically adjust fairness weights based on observed fairness violations. The system monitors fairness metrics during training and dynamically reweights observations that contribute to bias, without requiring manual hyperparameter tuning. This self-adjusting mechanism reduces sensitivity to hyperparameter settings while maintaining effective bias mitigation.
Solution Approach 2:
The patent applies feedback by continuously monitoring fairness constraints during the training process and using this information to adjust training weights. The system provides feedback loops where fairness violations are detected, weights are recalculated, and training is adjusted accordingly. This feedback mechanism ensures that the model converges to a fair solution without being overly sensitive to initial hyperparameter choices.
Data Source
AI summary
A computing device trains a fair machine learning model. A prediction model is trained to predict a target value. For a number of iterations, a weight vector is computed using the bound value based on fairness constraints defined for a fairness measure type; a weight value is assigned to each observation vector based on the target value and a sensitive attribute value; the prediction model is trained with each weighted observation vector to predict the target value; and a conditional moments vector is computed based on the fairness constraints and the target and sensitive attribute values. Conditional moments difference values are computed. When the conditional moments difference values indicate to adjust the bound value, the bound value is updated and the process is repeated with the bound value replaced with the updated bound value until the conditional moments difference values indicate no further adjustment of the bound value is needed.