Bayesian Neural Network Fairness via Aleatoric Uncertainty Weighting
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Solution Overview
Problem
The challenge in machine learning is to balance fairness and utility in classification tasks, as increasing fairness can reduce predictive accuracy and maximizing utility may prioritize a majority group over minority groups, leading to biased outcomes.
Innovation Solution
The Guided Algorithm for Integrating Aleatory (GAIA) framework addresses this challenge by employing aleatoric uncertainty to balance fairness and predictive performance. It uses Bayesian Neural Networks to generate aleatoric and epistemic uncertainty estimates, assigns weights to samples based on these estimates, and generates a conditioned training dataset to optimize the fairness-utility trade-off.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fairness is optimized to avoid discrimination against specific groups, then algorithmic fairness is improved, but predictive accuracy deteriorates
Solution Approach 1:
The patent applies local quality by differentiating treatment based on aleatoric uncertainty levels of individual samples. Samples with low aleatoric uncertainty (high confidence predictions) are prioritized for fairness optimization, while samples with high aleatoric uncertainty (low confidence predictions) are treated differently. This allows the model to achieve fairness where predictable while maintaining accuracy where uncertain, resolving the contradiction between overall fairness and overall accuracy.
Solution Approach 2:
The patent changes the parameter of sample weighting based on aleatoric uncertainty estimates. By adjusting the weight assigned to each training sample according to its aleatoric uncertainty level, the model can shift the optimization balance. Samples with lower uncertainty receive higher weights for fairness constraints, while samples with higher uncertainty receive lower weights, enabling simultaneous optimization of both fairness and accuracy metrics.
2Measurement precision
If utility is maximized to achieve high predictive accuracy, then predictive performance is improved, but fairness deteriorates as majority groups are prioritized
Solution Approach 1:
The patent applies local quality by differentiating treatment based on aleatoric uncertainty levels of individual samples. Samples with low aleatoric uncertainty (high confidence predictions) are prioritized for fairness optimization, while samples with high aleatoric uncertainty (low confidence predictions) are treated differently. This allows the model to achieve fairness where predictable while maintaining accuracy where uncertain, resolving the contradiction between overall fairness and overall accuracy.
Solution Approach 2:
The patent changes the parameter of sample weighting based on aleatoric uncertainty estimates. By adjusting the weight assigned to each training sample according to its aleatoric uncertainty level, the model can shift the optimization balance. Samples with lower uncertainty receive higher weights for fairness constraints, while samples with higher uncertainty receive lower weights, enabling simultaneous optimization of both fairness and accuracy metrics.
3Measurement precision
If more data is collected to improve predictive accuracy, then utility is improved, but the complexity of handling data limitations for underrepresented groups increases
Solution Approach 1:
The patent extracts and isolates the aleatoric uncertainty component from the overall model uncertainty. By separating aleatoric uncertainty (data-related) from epistemic uncertainty (model-related), the system can specifically address data limitations for underrepresented groups without complicating the entire data handling pipeline. This extraction allows targeted intervention on problematic data regions while maintaining overall model simplicity.
Data Source
AI summary
A method for training an artificial intelligence (AI) model includes receiving a training dataset and utilizing processing circuitry to train a Bayesian Neural Network (BNN) based on the dataset and a selected training algorithm including backpropagation. The method estimates aleatoric and epistemic uncertainty for each sample in the dataset. Based on these uncertainty estimates, weights are assigned to the samples, prioritizing those with lower aleatoric uncertainty. A conditioned training dataset is generated by increasing the weights of these prioritized samples. The AI model is then trained using this conditioned dataset. Finally, the pre-trained AI model outputs a prediction. This approach improves model performance by focusing on data with more predictable characteristics, reducing bias and enhancing prediction reliability.