Fairness-Aware Data Distillation for Bias Mitigation
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Solution Overview
Problem
Current data distillation methods for machine learning models inherit and may amplify biases from large-scale datasets, leading to unfair models, particularly affecting groups like gender and ethnicity.
Innovation Solution
The implementation of fairness-aware data distillation systems that utilize demographic parity and equalized odds processes to mitigate bias by calculating and updating a distilled dataset with matching losses, ensuring fair model training through methods like stochastic gradient descent and specific loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data distillation is performed using traditional methods, then model training efficiency is improved, but bias from the original dataset is inherited and amplified
Solution Approach 1:
The patent converts the harmful bias in the original dataset into a beneficial filtering mechanism by using fairness loss functions to identify and remove biased samples during distillation. The bias that would normally be inherited is instead used as a signal to guide the selection of fair representative samples, transforming the problem into a solution.
Solution Approach 2:
The patent introduces fairness-aware parameters including demographic parity loss and equalized odds loss to modify the traditional distillation objective function. By changing the optimization parameters to include fairness constraints, the distillation process simultaneously achieves efficiency while preventing bias inheritance through mathematical regularization terms.
2Object-generated harmful factors
If fairness constraints are added to data distillation, then bias mitigation is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial fairness constraints by selectively enforcing demographic parity and equalized odds on specific sensitive attributes and target variables rather than all possible combinations. This partial application of fairness principles reduces computational overhead while still achieving meaningful bias mitigation for the most critical fairness issues.
Solution Approach 2:
The patent introduces intermediary fairness loss functions that act as mediators between the original dataset and the distilled dataset. These loss functions serve as computational intermediaries that translate fairness requirements into practical optimization objectives, simplifying the overall computational process while maintaining fairness guarantees.
3Measurement precision
If fairness loss functions are calculated during distillation, then prediction probability fairness is improved, but training time increases
Solution Approach 1:
The patent performs preliminary fairness analysis and sample selection before the main distillation process by pre-computing fairness metrics and identifying potentially biased samples. This preliminary action reduces the computational burden during actual training by pre-filtering the dataset and pre-calculating fairness weights, thereby maintaining prediction probability fairness while reducing overall training time.
Data Source
AI summary
A method may include: receiving an original dataset and a sensitive attribute; initializing a distilled dataset from the original dataset; initializing a classification model; sampling original dataset data and an original dataset label from the original dataset, and distilled dataset data and a distilled dataset label from the distilled dataset; providing the sampled original dataset data from the original dataset to the classification model, resulting in an original dataset prediction probability; providing the sampled distilled dataset data from the distilled dataset to the classification model, resulting in a distilled dataset prediction probability; calculating a distilled dataset cross-entropy loss for the distilled dataset prediction probability and the distilled dataset label; calculating distilled dataset gradients for the distilled dataset cross-entropy loss; calculating a distance between the gradients as a matching loss; and updating the distilled dataset with the matching loss.