How to modify decision trees for fairness-aware learning?

How to Modify Decision Trees for Fairness-Aware Learning

Decision trees, widely used in machine learning for their interpretability and efficiency, can unfortunately reinforce bias if sensitive attributes—such as race, gender, or age—significantly influence splits in the tree structure. Fairness-aware learning, which aims to create unbiased, equitable models, proposes several methods for modifying decision trees to balance accuracy with fairness. Here, we discuss some innovative approaches for adapting decision trees in fairness-aware learning.

Quick Example: Imagine a decision tree used by a bank to predict loan approvals. If the tree splits based on income and education level, but these factors are correlated with race in the dataset, the model might unfairly deny loans to certain racial groups. A fairness-aware decision tree would modify this process by either adjusting how splits are made or altering leaf node decisions to ensure that race does not disproportionately affect the outcomes.

Main Explanation: Why Do Decision Trees Need Fairness Modifications?

Decision trees learn by splitting data based on features that maximize predictive performance. However, if sensitive attributes (like gender or race) influence these splits, the model may inadvertently discriminate against certain groups. For example, if a dataset shows a correlation between income and race, a decision tree might favor one racial group over another when predicting outcomes like loan approvals or job offers. To address this issue, fairness-aware learning modifies decision trees in two primary ways:

1. In-Processing Modifications

These methods adjust the decision-making process during training.

Fair Information Gain: Traditional decision trees use information gain or Gini impurity to decide splits. Fairness-aware methods introduce a new criterion called Fair Information Gain (FIG), which balances both predictive performance and fairness. FIG ensures that splits do not disproportionately favor one group over another by considering both accuracy and fairness when choosing attributes for splitting13.

2. Post-Processing Modifications

These methods adjust the trained decision tree.

Fairness-Aware Decision Tree Editing (FADE): This approach revises an already trained decision tree by modifying its structure—either deleting biased branches or relabeling leaf nodes—to ensure fair outcomes without significantly affecting predictive performance

1. Modifying the Attribute Selection Process

A key part of building a decision tree involves selecting attributes that maximize information gain, forming the best possible splits at each node. However, for fairness-aware models, attribute selection can be adapted to consider the impact on fairness as well as accuracy. The Fair-C4.5 algorithm, for instance, extends the classic C4.5 tree by combining fairness metrics with gain ratio during attribute selection. This dual focus helps reduce bias while retaining strong predictive performance. Another approach, the FFTree algorithm, screens attributes using multiple fairness metrics to meet predefined fairness thresholds, ensuring only attributes that maintain fair outcomes are chosen for the tree.

2. Editing Existing Decision Trees

Instead of altering the attribute selection process, fairness can also be integrated post-training. Fairness-Aware Decision Tree Editing (FADE) modifies trained decision trees to satisfy fairness constraints, aiming to minimize structural changes and prediction shifts from the original tree. FADE uses dissimilarity measures like prediction discrepancy and edit distance to assess the degree of change required, then optimizes these edits using mixed-integer linear optimization (MILO). This method maintains the model's interpretability while reducing bias, as FADE primarily alters nodes that introduce unfairness, ensuring that edited models reflect a fairer decision-making process.

3. Adversarial Training

Adversarial training, a technique often applied to enhance model robustness, is also useful for fairness-aware learning. Fairness-Aware Tree Training (FATT) applies an adversarial approach to decision trees, focusing on maximizing fairness and accuracy simultaneously. FATT leverages Meta-Silvae, a decision tree ensemble method, to create robust decision boundaries while limiting unfair bias. By identifying fairness metrics that measure the similarity of outcomes across groups, FATT reduces the likelihood that slight perturbations in data will result in unfair predictions, thus promoting individual fairness.

4. Using Fairness Metrics as Hints

Incorporating insights from fairness-aware models can inform the hyperparameter tuning of standard decision tree algorithms. Fair models like those created by FATT often display specific characteristics—such as maximum depth or minimum samples per leaf—that can be used as "hints" in training conventional decision trees. By aligning the hyperparameters of traditional decision tree models with these fair model characteristics, practitioners can produce models that balance accuracy with fairness without needing to apply a fairness-specific algorithm.

Conclusion

Fairness-aware learning involves balancing multiple fairness metrics, which can sometimes capture different forms of discrimination. Hence, using a combination of these metrics during training and post-processing of decision trees can help reduce both direct and indirect bias. These fairness-aware modifications—whether through attribute selection, post-processing, adversarial training, or fairness-inspired hyperparameter tuning—are essential in making decision trees both powerful and equitable tools in predictive modeling. By mitigating bias, fairness-aware decision trees support ethical, inclusive, and socially responsible AI.