Fair Adversarial Networks

Description

An in-processing fairness technique that employs adversarial training with dual neural networks to learn fair representations. The method consists of a predictor network that learns the main task whilst an adversarial discriminator network simultaneously attempts to predict sensitive attributes from the predictor's hidden representations. Through this adversarial min-max game, the predictor is incentivised to learn features that are informative for the task but statistically independent of protected attributes, effectively removing bias at the representation level in deep learning models.

Example Use Cases

Fairness

Training a facial recognition system that maintains high accuracy for person identification whilst ensuring equal performance across different ethnic groups, using adversarial training to remove race-related features from learned representations.

Transparency

Developing a resume screening neural network that provides transparent evidence of bias mitigation by demonstrating that learned features cannot predict gender, whilst maintaining predictive performance for job suitability assessment.

Reliability

Creating a medical image analysis model that achieves reliable diagnostic performance across patient demographics by using adversarial debiasing to ensure age and gender information cannot be extracted from diagnostic features.

Limitations

Implementation complexity is high, requiring careful design of adversarial loss functions and balancing multiple competing objectives during training.
Sensitive to hyperparameter choices, particularly the trade-off weights between prediction accuracy and adversarial loss, which require extensive tuning.
Adversarial training can be unstable, with potential for mode collapse or failure to converge, especially in complex deep learning architectures.
Interpretability of fairness improvements can be limited, as it may be difficult to verify that sensitive attributes are truly removed from learned representations.
Computational overhead is significant due to training two networks simultaneously, increasing both training time and resource requirements.

Resources

Fair Adversarial Networks

Research Paper•George Cevora•Feb 23, 2020

Demonstrating Rosa: the fairness solution for any Data Analytic pipeline

Research Paper•Kate Wilkinson and George Cevora•Feb 28, 2020

Triangular Trade-off between Robustness, Accuracy, and Fairness in Deep Neural Networks: A Survey

Documentation•Jingyang Li and Guoqiang Li•Feb 10, 2025

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Research Paper•Resmi Ramachandranpillai et al.•Dec 14, 2023

Related Techniques

Name	Description	Assurance Goals
Red Teaming	Red teaming involves systematic adversarial testing of AI/ML systems by dedicated specialists who attempt to identify flaws, vulnerabilities, harmful outputs, and ways to circumvent safety measures. Drawing from cybersecurity practices, red teams employ diverse attack vectors including prompt injection, adversarial examples, edge case exploitation, social engineering scenarios, and goal misalignment probes. Unlike standard testing that validates expected behaviour, red teaming specifically seeks to break systems through creative and adversarial approaches, revealing non-obvious risks and failure modes that could be exploited maliciously or cause harm in deployment.	Safety Reliability Fairness Security
SHapley Additive exPlanations	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an importance score to every feature, indicating whether it pushes the prediction towards or away from the average. The method systematically evaluates how predictions change as features are included or excluded, drawing on game theory concepts to ensure a fair distribution of contributions.	Explainability Fairness Reliability
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
Counterfactual Fairness Assessment	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's protected attributes (race, gender, age) were different, whilst keeping all other causally legitimate factors constant. The technique requires constructing a causal graph that maps relationships between variables, then using do-calculus or structural causal models to simulate counterfactual scenarios. For example, it asks: 'Would this loan application still be approved if the applicant were a different race, holding constant their actual qualifications and economic circumstances?' This individual-level fairness criterion helps identify when decisions depend improperly on protected characteristics.	Fairness
Anomaly Detection	Anomaly detection identifies unusual behaviours, inputs, or outputs that deviate significantly from established normal patterns using statistical, machine learning, or rule-based methods. Applied to AI/ML systems, it serves as a continuous monitoring mechanism that can flag unexpected model predictions, suspicious input patterns, data drift, adversarial attacks, or operational malfunctions. By establishing baselines of normal system behaviour and alerting when deviations exceed predefined thresholds, organisations can detect potential security threats, model degradation, fairness violations, or system failures before they cause significant harm.	Safety Reliability Fairness Security
Datasheets for Datasets	Datasheets for datasets establish comprehensive documentation standards for datasets, systematically recording creation methodology, data composition, collection procedures, preprocessing transformations, intended applications, potential biases, privacy considerations, and maintenance protocols. These structured documents enhance dataset transparency by providing essential context for appropriate usage, enabling informed decisions about dataset suitability for specific tasks, supporting bias detection and mitigation efforts, ensuring compliance with data protection regulations, and promoting responsible data stewardship throughout the entire data lifecycle from collection to disposal.	Transparency Fairness Privacy

Fair Adversarial Networks

Description

Example Use Cases

Fairness

Transparency

Reliability

Limitations

Resources

Fair Adversarial Networks

Demonstrating Rosa: the fairness solution for any Data Analytic pipeline

Triangular Trade-off between Robustness, Accuracy, and Fairness in Deep Neural Networks: A Survey

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks

Related Techniques

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