Adaptive Sensitive Reweighting

Description

Adaptive Sensitive Reweighting dynamically adjusts the importance of training examples during model training based on real-time performance across different demographic groups. Unlike traditional static reweighting that fixes weights at the start, this technique continuously monitors fairness metrics and automatically increases the weight of examples from underperforming groups whilst decreasing weights for overrepresented groups. The adaptive mechanism prevents models from perpetuating historical biases by ensuring balanced learning across all demographics throughout the training process.

Example Use Cases

Fairness

Training speech recognition systems that adapt weights during training to ensure consistent accuracy across different accents, dialects, and linguistic backgrounds, preventing models from favouring dominant accent groups in the training data.

Developing hiring algorithms that dynamically adjust training example weights to maintain consistent evaluation performance across demographic groups, ensuring the model doesn't learn to favour candidates from overrepresented backgrounds.

Reliability

Building medical diagnostic models that adaptively reweight patient examples during training to ensure reliable performance across different age groups, ethnicities, and socioeconomic backgrounds, preventing healthcare disparities.

Limitations

Training instability can occur when adaptive weight adjustments cause oscillations between demographic groups, potentially preventing convergence if reweighting parameters are not carefully tuned.
Computational overhead increases significantly due to continuous monitoring of fairness metrics across groups during training, requiring additional memory and processing time.
Risk of overfitting to specific demographic subgroups if the adaptation mechanism becomes too aggressive in correcting for observed performance disparities during training.
Requires careful hyperparameter tuning for adaptation rates and fairness thresholds, making the technique sensitive to configuration choices that may not generalise across different datasets.
May inadvertently harm overall model performance if the reweighting process prioritises fairness at the expense of learning important patterns that benefit all groups.

Resources

Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

Research Paper•Krasanakis, Emmanouil et al.•Apr 23, 2018

Original paper introducing adaptive sensitive reweighting technique using CULEP model for bias mitigation in classification tasks

AIF360: A comprehensive set of fairness metrics for datasets and machine learning models

Software Package

IBM's comprehensive fairness toolkit including implementations of various reweighting techniques and bias mitigation methods

Fairlearn: A toolkit for assessing and improving fairness in machine learning

Software Package

Microsoft's open-source toolkit providing reweighting and other bias mitigation algorithms with comprehensive documentation

Causal Fairness-Guided Dataset Reweighting using Neural Networks

Research Paper•Zhao, Xuan et al.•Nov 17, 2023

Recent research on causal fairness-guided dataset reweighting using neural networks to address fairness from causal perspective

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MLflow Experiment Tracking	MLflow is an open-source platform that tracks machine learning experiments by automatically logging parameters, metrics, models, and artifacts throughout the ML lifecycle. It provides a centralised repository for comparing different experimental runs, reproducing results, and managing model versions. Teams can track hyperparameters, evaluation metrics, model files, and execution environment details, creating a comprehensive audit trail that supports collaboration, reproducibility, and regulatory compliance across the entire machine learning development process.	Transparency Reliability
Reweighing	Reweighing is a pre-processing technique that mitigates bias by assigning different weights to training examples based on their group membership and class label. The weights are calculated to ensure that privileged and unprivileged groups have equal influence on the model's training process, effectively balancing the dataset without altering the feature values themselves. This helps to train fairer models by correcting for historical imbalances in how different groups are represented in the data.	Fairness Transparency Reliability
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Relabelling	A preprocessing fairness technique that modifies class labels in training data to achieve equal positive outcome rates across protected groups. Also known as 'data massaging', this method identifies instances that contribute to discriminatory patterns and flips their labels (from positive to negative or vice versa) to balance the proportion of positive outcomes between demographic groups. The technique aims to remove historical bias from training data whilst preserving the overall class distribution, enabling standard classifiers to learn from discrimination-free datasets.	Fairness Transparency Reliability
Calibration with Equality of Opportunity	A post-processing fairness technique that adjusts model predictions to achieve equal true positive rates across protected groups whilst maintaining calibration within each group. The method addresses fairness by ensuring that qualified individuals from different demographic groups have equal chances of receiving positive predictions, whilst preserving the meaning of probability scores within each group. This technique attempts to balance the competing objectives of group fairness and accurate probability estimation.	Fairness Transparency Reliability