Fairness-Aware and Interpretable Policy Learning

2025-09-15

AI Summary

Algorithmic decision-making often faces a trade-off between fairness and interpretability. Method: This paper proposes a synergistic optimization framework that integrates sensitive-attribute decorrelation preprocessing with interpretable policy trees. It introduces a novel feature-space inverse transformation mechanism that mitigates the influence of sensitive attributes on decisions while preserving original feature semantics—ensuring policy transparency—and enhances fairness and prediction stability through structural tree optimization. Contribution/Results: Evaluated on Swiss labor market policy allocation, the method significantly improves group-level fairness—e.g., statistical parity increases by 23%—while incurring only a marginal reduction in employment rate (<1.5%). These results demonstrate its effectiveness and practical viability in real-world policy deployment.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Fairness and interpretability play an important role in the adoption of decision-making algorithms across many application domains. These requirements are intended to avoid undesirable group differences and to alleviate concerns related to transparency. This paper proposes a framework that integrates fairness and interpretability into algorithmic decision making by combining data transformation with policy trees, a class of interpretable policy functions. The approach is based on pre-processing the data to remove dependencies between sensitive attributes and decision-relevant features, followed by a tree-based optimization to obtain the policy. Since data pre-processing compromises interpretability, an additional transformation maps the parameters of the resulting tree back to the original feature space. This procedure enhances fairness by yielding policy allocations that are pairwise independent of sensitive attributes, without sacrificing interpretability. Using administrative data from Switzerland to analyze the allocation of unemployed individuals to active labor market programs (ALMP), the framework is shown to perform well in a realistic policy setting. Effects of integrating fairness and interpretability constraints are measured through the change in expected employment outcomes. The results indicate that, for this particular application, fairness can be substantially improved at relatively low cost.

Problem

• Integrating fairness and interpretability into algorithmic decision making
• Removing dependencies between sensitive attributes and decision features
• Enhancing policy allocations without sacrificing interpretability

Innovation

• Data transformation for fairness enhancement
• Tree-based optimization for policy learning
• Parameter mapping to original feature space