Attribute Removal (Fairness Through Unawareness)

Description

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.

Example Use Cases

Fairness

Removing gender, race, and age attributes from hiring algorithms to prevent direct discrimination, whilst acknowledging that indirect bias may persist through correlated features like education institution or postal code.

Excluding protected demographic attributes from credit scoring models to comply with fair lending regulations, ensuring no explicit consideration of race, gender, or ethnicity in loan approval decisions.

Building medical diagnosis models that exclude patient race and ethnicity to prevent biased treatment recommendations, whilst ensuring clinical decisions are based solely on medical indicators and symptoms.

Transparency

Creating transparent regulatory reporting systems that demonstrate compliance by explicitly documenting which protected attributes have been excluded from decision-making algorithms, providing clear audit trails for regulatory review.

Limitations

Proxy discrimination remains a major concern as seemingly neutral features (education, postal code, previous employment) may strongly correlate with protected attributes, perpetuating indirect bias.
Intersectional bias cannot be addressed through simple attribute removal, as complex interactions between multiple demographic characteristics may create compounding discrimination effects.
Legal and regulatory compliance may be insufficient, as many jurisdictions require demonstrating disparate impact absence rather than simply removing protected attributes from models.
Identifying all potential proxy variables is practically impossible, especially with high-dimensional data where subtle correlations with protected attributes may exist in unexpected features.
Performance degradation may occur if removed attributes contain legitimate predictive information, creating tension between fairness objectives and model accuracy requirements.

Resources

Fairness Through Awareness

Research Paper•Dwork, Cynthia et al.•Jan 1, 2012

Foundational paper introducing fairness through awareness concept and demonstrating limitations of fairness through unawareness

Fairness Constraints: Mechanisms for Fair Classification

Research Paper•Zafar, Muhammad Bilal et al.•Jul 19, 2015

Comprehensive analysis of fairness approaches including attribute removal limitations and proxy discrimination challenges

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

Software Package

Microsoft's comprehensive fairness toolkit with preprocessing methods including attribute removal and proxy detection tools

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

Documentation•Kearns, Michael and Roth, Aaron•Nov 1, 2019

Accessible book covering fairness through unawareness concepts and practical considerations for practitioners

Related Techniques

Name	Description	Assurance Goals
Sobol Indices	Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through global sensitivity analysis. The technique calculates first-order indices (individual feature contributions) and total-order indices (including all interaction effects involving that feature). By systematically sampling the input space and decomposing output variance, Sobol Indices reveal which features drive model uncertainty and which interactions between features are most important for predictions.	Explainability Fairness
Contextual Decomposition	Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from individual inputs and their interactions. Unlike simpler attribution methods, it separates the direct contribution of specific words or phrases from the contextual effects of surrounding words. This is particularly useful for understanding how sequential models process language, as it can identify whether a word's influence comes from its individual meaning or from its interaction with nearby words in the sequence.	Explainability Transparency
Reject Option Classification	A post-processing fairness technique that modifies predictions in regions of high uncertainty to favour disadvantaged groups and achieve fairness objectives. The method identifies a 'rejection region' where the model's confidence is low (typically near the decision boundary) and reassigns predictions within this region to benefit underrepresented groups. By leveraging model uncertainty, this approach can improve fairness metrics like demographic parity or equalised odds whilst minimising changes to confident predictions, thus preserving overall accuracy for cases where the model is certain.	Fairness Reliability Transparency
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
Synthetic Data Generation	Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions, and relationships of real data whilst containing no actual records from real individuals. The technique encompasses various approaches including generative adversarial networks (GANs), variational autoencoders (VAEs), statistical sampling methods, and privacy-preserving techniques like differential privacy. Beyond privacy protection, synthetic data serves multiple purposes: augmenting limited datasets, balancing class distributions, testing model robustness, enabling data sharing across organisations, and supporting fairness assessments by generating representative samples for underrepresented groups.	Privacy Fairness Reliability Safety
Contrastive Explanation Method	The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what makes a prediction distinctive. It identifies 'pertinent negatives' (minimal features that could be removed to change the prediction) and 'pertinent positives' (minimal features that must be present to maintain the prediction). This approach helps users understand not just what led to a decision, but what would need to change to achieve a different outcome, providing actionable insights for decision-making.	Explainability Transparency