Bayesian Fairness Regularization

Description

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.

Example Use Cases

Fairness

Using Fair Bayesian Optimization to tune hyperparameters of credit risk models, automatically balancing predictive accuracy with fairness constraints across different demographic groups whilst accounting for uncertainty in both model performance and fairness requirements.

Implementing Bayesian neural networks with fairness-aware priors for hiring recommendation systems, where uncertainty in fairness constraints is modeled probabilistically to ensure robust fair decision-making across different candidate populations.

Developing insurance premium calculation models using Bayesian fairness regularization to ensure actuarially sound pricing that meets regulatory fairness requirements, with probabilistic modeling of both risk assessment accuracy and demographic equity.

Reliability

Applying Bayesian regularization techniques to medical diagnosis models to ensure reliable performance across patient demographics, using probabilistic constraints to maintain consistent diagnostic accuracy whilst preventing algorithmic bias in healthcare delivery.

Limitations

Prior selection challenges make it difficult to specify appropriate prior distributions for fairness constraints, requiring domain expertise and potentially leading to suboptimal or biased outcomes if priors are poorly chosen.
Computational complexity increases significantly due to Bayesian inference requirements, including sampling methods, variational inference, or optimization over probability distributions, making the approach less scalable for large datasets.
Sensitivity to hyperparameters affects both the Bayesian inference process and fairness regularization terms, requiring careful tuning of multiple parameters that control the trade-off between accuracy, fairness, and computational efficiency.
Convergence and stability issues may arise in Bayesian optimization with fairness constraints, particularly when fairness objectives conflict with performance objectives or when the constraint space becomes highly complex.
Limited theoretical understanding exists for the interaction between Bayesian uncertainty quantification and fairness constraints, making it challenging to provide guarantees about both predictive performance and fairness under uncertainty.

Resources

Research Papers

Bayesian fairness

Christos Dimitrakakis et al.•May 31, 2017

We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of {\em Bayesian fairness} as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced by Kleinberg et al (2016), we show how a Bayesian perspective can lead to well-performing, fair decision rules even under high uncertainty.

Fair Bayesian Optimization

Valerio Perrone et al.•Jun 9, 2020

Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a single family of ML models and a specific definition of fairness, limiting their adaptibility in practice. We introduce a general constrained Bayesian optimization (BO) framework to optimize the performance of any ML model while enforcing one or multiple fairness constraints. BO is a model-agnostic optimization method that has been successfully applied to automatically tune the hyperparameters of ML models. We apply BO with fairness constraints to a range of popular models, including random forests, gradient boosting, and neural networks, showing that we can obtain accurate and fair solutions by acting solely on the hyperparameters. We also show empirically that our approach is competitive with specialized techniques that enforce model-specific fairness constraints, and outperforms preprocessing methods that learn fair representations of the input data. Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters. Finally, we study the relationship between fairness and the hyperparameters selected by BO. We observe a correlation between regularization and unbiased models, explaining why acting on the hyperparameters leads to ML models that generalize well and are fair.

Fairness-Aware Classifier with Prejudice Remover Regularizer

Toshihiro Kamishima et al.•Sep 24, 2012

Software Packages

fgp

Jun 21, 2019

Fair Gaussian Processes

Related Techniques

Name	Description	Assurance Goals
Sensitivity Analysis for Fairness	Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or their proxies are perturbed whilst holding other factors constant. The technique involves creating counterfactual instances by modifying potentially discriminatory features (race, gender, age) or their correlates (zip code, names, education institutions) and measuring the resulting prediction differences. This controlled perturbation approach quantifies the degree to which protected characteristics influence model decisions, helping detect both direct discrimination and indirect bias through proxy variables even when sensitive attributes are not explicitly used as model inputs.	Fairness
Fairness GAN	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.	Fairness Privacy Reliability
Attribute Removal (Fairness Through Unawareness)	Attribute Removal (Fairness Through Unawareness) ensures fairness by completely excluding protected attributes such as race, gender, or age from the model's input features. While this approach prevents direct discrimination, it may not eliminate bias if other features are correlated with protected attributes (proxy discrimination). This technique represents the most basic fairness intervention but often needs to be combined with other approaches to address indirect bias through seemingly neutral features.	Fairness Transparency
Out-of-Domain Detection	Out-of-domain (OOD) detection identifies user inputs that fall outside an AI system's intended domain or capabilities, enabling graceful handling rather than generating unreliable responses. This technique trains classifiers or uses embedding-based methods to recognise when queries require knowledge or capabilities the system doesn't possess. OOD detection enables systems to explicitly decline, redirect, or request clarification rather than hallucinating answers or applying inappropriate reasoning to unfamiliar domains.	Reliability Safety Transparency