PC-Fairness: A Unified Framework for Measuring Causality-based Fairness (1910.12586v1)

Abstract: A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.

Overview of PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

The paper introduces a novel concept of path-specific counterfactual fairness (PC fairness) which serves as a comprehensive framework for evaluating fairness within machine learning decisions by leveraging causality principles. This framework is designed to address the identifiability challenges inherent in causality-based fairness assessments, a critical hurdle that has limited practical application in real-world contexts.

Core Contributions

1. Unified Definition: The authors propose PC fairness, unifying previous causality-based fairness definitions such as total effect, direct/indirect discrimination, and counterfactual fairness. PC fairness evaluates fairness by examining causal paths with specific counterfactual conditions, thereby providing a broader and more flexible evaluation structure.
2. Identifiability Barrier: The paper highlights identifiability constraints in observational data, which make it difficult to measure and apply causality-based fairness norms effectively. Identifiability issues arise when causal effects cannot be uniquely determined due to limitations in data structure or availability.
3. Optimization Framework: A significant contribution is the development of a constrained optimization method to bound PC fairness in scenarios where identifiability is compromised. The technique involves expressing fairness measures as constrained optimization problems, allowing researchers to explore causal models systematically to establish tight fairness bounds.

Numerical Results and Validation

The proposed framework is validated through experiments using synthetic and real-world datasets. Results indicate that the framework accurately measures fairness under varying conditions and provides tighter bounds compared to prior methods. These measurements are significant in identifying degrees of fairness and ensuring predictive models are non-discriminatory.

Implications for Future Research and AI Development

• Enhanced Fairness Assessments: By establishing a unified fairness framework, the paper paves the way for more holistic assessments of algorithmic fairness, potentially influencing how fairness standards are implemented in AI systems.
• Scalability and Application: While the framework offers theoretical robustness, its application to large-scale data and complex causal graphs demands further exploration, particularly in optimizing computation.
• Expansion of Ethical AI: This research underpins efforts to advance ethical AI practices by providing mechanisms that can be integrated into algorithm design to enforce fairness across diverse demographic profiles. Future directions could include embedding fairness constraints directly into learning algorithms.

In summation, this paper contributes to the discourse on fairness in AI by combining causal inference with fairness assessment. It offers a potent methodological advance that could reshape how fairness is measured and ensured in machine learning applications.