Fairness GAN

Description

A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by learning to generate data representations that preserve utility whilst obfuscating protected attributes. Unlike traditional GANs, Fairness GANs incorporate fairness constraints into the training objective, ensuring that the generated data maintains statistical parity across demographic groups. The technique can be used for data augmentation to balance underrepresented groups or to create privacy-preserving synthetic datasets that remove demographic bias from training data.

Example Use Cases

Fairness

Generating balanced synthetic datasets for medical research by creating additional samples from underrepresented demographic groups, ensuring equal representation across ethnicity and gender whilst maintaining the statistical properties needed for robust model training.

Privacy

Creating privacy-preserving synthetic datasets for financial services that remove demographic identifiers whilst preserving the underlying patterns needed for credit risk assessment, allowing secure data sharing between institutions without exposing sensitive customer information.

Reliability

Augmenting recruitment datasets by generating synthetic candidate profiles that balance gender and ethnicity representation, ensuring reliable model performance across all demographic groups when real-world data exhibits significant imbalances.

Limitations

GAN training is notoriously difficult to stabilise, with potential for mode collapse or failure to converge, especially when additional fairness constraints are imposed.
Ensuring fairness in generated data may come at the cost of data utility, potentially reducing the quality or realism of synthetic samples.
Requires large datasets to train both generator and discriminator networks effectively, limiting applicability in data-scarce domains.
Evaluation complexity is high, as it requires assessing both the quality of generated data and the preservation of fairness properties across demographic groups.
May inadvertently introduce new biases if the fairness constraints are not properly specified or if the training data itself contains subtle biases.

Resources

Fairness GAN

Research Paper•Prasanna Sattigeri et al.•May 24, 2018

Fair GANs through model rebalancing for extremely imbalanced class distributions

Research Paper•Anubhav Jain, Nasir Memon, and Julian Togelius•Aug 16, 2023

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

Research Paper•Ning Yu et al.•Apr 7, 2020

Related Techniques

Name	Description	Assurance Goals
Bayesian Fairness Regularization	Bayesian Fairness Regularization incorporates fairness constraints into machine learning models through Bayesian methods, treating fairness as a prior distribution or regularization term. This approach includes techniques like Fair Bayesian Optimization that use constrained optimization to tune model hyperparameters whilst enforcing fairness constraints, and methods that add regularization terms to objective functions to penalize discriminatory predictions. The technique allows for probabilistic interpretation of fairness constraints and can account for uncertainty in both model parameters and fairness requirements.	Fairness Reliability
Prediction Intervals	Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the true value will likely fall between X and Y with Z% confidence'. For example, instead of predicting 'house price: £300,000', a prediction interval might say 'house price: £280,000 to £320,000 with 95% confidence'. This technique works by calculating upper and lower bounds that account for both model uncertainty (how confident the model is) and inherent randomness in the data. Prediction intervals are crucial for informed decision-making, as they help users understand the reliability and precision of predictions, enabling better risk assessment and planning.	Reliability Transparency Fairness
Equalised Odds Post-Processing	A post-processing fairness technique based on Hardt et al.'s seminal work that adjusts classification thresholds after model training to achieve equal true positive rates and false positive rates across demographic groups. The method uses group-specific decision thresholds, potentially with randomisation, to satisfy the equalised odds constraint whilst preserving model utility. This approach enables fairness mitigation without retraining, making it applicable to existing deployed models or when training data access is restricted.	Fairness Transparency Reliability
Model Cards	Model cards are standardised documentation frameworks that systematically document machine learning models through structured templates. The templates cover intended use cases, performance metrics across different demographic groups and operating conditions, training data characteristics, evaluation procedures, limitations, and ethical considerations. They serve as comprehensive technical specifications that enable informed model selection, prevent inappropriate deployment, support regulatory compliance, and facilitate fair assessment by providing transparent reporting of model capabilities and constraints across diverse populations and scenarios.	Transparency Fairness Safety
Deep Ensembles	Deep ensembles combine predictions from multiple neural networks trained independently with different random initializations to capture epistemic uncertainty (model uncertainty). By training several models on the same data with different starting points, the ensemble reveals how much the model's predictions depend on training randomness. The disagreement between ensemble members naturally indicates prediction uncertainty - when models agree, confidence is high; when they disagree, uncertainty is revealed. This approach provides more reliable uncertainty estimates, better out-of-distribution detection, and improved calibration compared to single models.	Reliability Transparency Safety
Average Odds Difference	Average Odds Difference measures fairness by calculating the average difference in both false positive rates and true positive rates between different demographic groups. This metric captures how consistently a model performs across groups for both positive and negative predictions. A value of 0 indicates perfect fairness under the equalized odds criterion, while larger absolute values indicate greater disparities in model performance between groups.	Fairness Reliability

Fairness GAN

Description

Example Use Cases

Fairness

Privacy

Reliability

Limitations

Resources

Fairness GAN

Fair GANs through model rebalancing for extremely imbalanced class distributions

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

Related Techniques

Tags