Fair Transfer Learning

Description

An in-processing fairness technique that adapts pre-trained models from one domain to another whilst explicitly preserving fairness constraints across different contexts or populations. The method addresses the challenge that fairness properties may not transfer when adapting models to new domains with different demographic compositions or data distributions. Fair transfer learning typically involves constraint-aware fine-tuning, domain adaptation techniques, or adversarial training that maintains equitable performance across groups in the target domain, ensuring that bias mitigation efforts carry over from source to target domains.

Example Use Cases

Fairness

Adapting a hiring algorithm trained on one country's recruitment data to another region whilst maintaining fairness across gender and ethnicity groups, ensuring equitable candidate evaluation despite different local demographic distributions and cultural contexts.

Transparency

Transferring a medical diagnosis model from urban hospital data to rural clinics whilst providing transparent evidence that fairness constraints are preserved across age, gender, and socioeconomic groups despite different patient populations and healthcare infrastructure.

Reliability

Adapting a fraud detection system from one financial market to another whilst ensuring reliable performance across customer demographics, maintaining consistent accuracy and fairness even when transaction patterns and customer characteristics differ between markets.

Limitations

Fairness properties achieved in the source domain may not translate directly to the target domain if demographic distributions or data characteristics differ significantly.
Requires careful hyperparameter tuning and constraint specification to balance fairness preservation with model performance in the new domain.
Implementation complexity is high, requiring expertise in both transfer learning techniques and fairness constraint optimisation methods.
May suffer from negative transfer effects where fairness constraints that worked well in the source domain actually harm performance in the target domain.
Evaluation challenges arise from needing to validate fairness across multiple domains and demographic groups simultaneously.

Resources

Segmenting across places: The need for fair transfer learning with satellite imagery

Research Paper•Miao Zhang et al.•Apr 9, 2022

Trustworthy Transfer Learning: A Survey

Documentation•Jun Wu and Jingrui He•Dec 18, 2024

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity

Research Paper•Josh Gardner et al.•May 1, 2023

Related Techniques

Name	Description	Assurance Goals
Preferential Sampling	A preprocessing fairness technique developed by Kamiran and Calders that addresses dataset imbalances by re-sampling training data with preference for underrepresented groups to achieve discrimination-free classification. This method modifies the training distribution by prioritising borderline objects (instances near decision boundaries) from underrepresented groups for duplication whilst potentially removing instances from overrepresented groups. Unlike relabelling approaches, preferential sampling maintains original class labels whilst creating a more balanced dataset that prevents models from learning biased patterns due to skewed group representation.	Fairness Reliability Transparency
Confidence Thresholding	Confidence thresholding creates decision boundaries based on model uncertainty scores, routing predictions into different handling workflows depending on their confidence levels. High-confidence predictions (e.g., above 95%) proceed automatically, whilst medium-confidence cases (e.g., 70-95%) may trigger additional validation or human review, and low-confidence predictions (below 70%) receive extensive oversight or default to conservative fallback actions. This technique enables organisations to maintain automated efficiency for clear-cut cases whilst ensuring appropriate human intervention for uncertain decisions, balancing operational speed with risk management across safety-critical applications.	Safety Reliability Transparency
Monte Carlo Dropout	Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to zero) during inference rather than just training. It performs multiple forward passes through the network with different random dropout patterns and collects the resulting predictions to form a distribution. Low variance across predictions indicates epistemic certainty (the model is confident), while high variance suggests epistemic uncertainty (the model is unsure). This technique transforms any dropout-trained neural network into a Bayesian approximation for uncertainty quantification.	Explainability Reliability
Equal Opportunity Difference	A fairness metric that quantifies discrimination by measuring the difference in true positive rates (recall) between protected and privileged groups. Based on Hardt et al.'s equality of opportunity framework, this metric computes the maximum difference in TPR across demographic groups, with a value of 0 indicating perfect fairness. The technique provides a mathematical measure of whether qualified individuals from different groups have equal chances of receiving positive predictions.	Fairness Transparency Reliability
Mean Decrease Impurity	Mean Decrease Impurity (MDI) quantifies a feature's importance in tree-based models (e.g., Random Forests, Gradient Boosting Machines) by measuring the total reduction in impurity (e.g., Gini impurity, entropy) across all splits where the feature is used. Features that lead to larger, more consistent reductions in impurity are considered more important, indicating their effectiveness in creating homogeneous child nodes and improving predictive accuracy.	Explainability Reliability
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