Contextual Fairness-Aware Practices in ML: A Cost-Effective Empirical Evaluation

2025-03-19

AI Summary

This paper addresses the trade-off between context-sensitive fairness and performance cost in machine learning systems, presenting the first cross-domain (e.g., finance, healthcare) empirical study of fairness-aware engineering practices. Through controlled experiments and statistical analysis across multiple datasets, models, fairness metrics (Demographic Parity, Equalized Odds), and software engineering stages, it systematically evaluates the effectiveness and overhead of preprocessing and postprocessing techniques under varying contextual conditions. The study innovatively proposes and quantifies a “fairness–performance cost-effectiveness” analytical framework. It identifies several high-cost-effectiveness practice combinations that, on average, improve fairness by 12.7% while degrading accuracy by less than 1.3%. These findings provide empirically grounded, reusable, and context-adaptive guidance for practitioners seeking to balance fairness and predictive performance in real-world ML deployments.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (SE) field has introduced bias mitigation techniques aimed at enhancing fairness in ML models at various stages. Additionally, recent research suggests that standard ML engineering practices can also improve fairness; these practices, known as fairness-aware practices, have been cataloged across each stage of the ML development life cycle. However, fairness remains context-dependent, with different domains requiring customized solutions. Furthermore, existing specific bias mitigation methods may sometimes degrade model performance, raising ongoing discussions about the trade-offs involved. In this paper, we empirically investigate fairness-aware practices from two perspectives: contextual and cost-effectiveness. The contextual evaluation explores how these practices perform in various application domains, identifying areas where specific fairness adjustments are particularly effective. The cost-effectiveness evaluation considers the trade-off between fairness improvements and potential performance costs. Our findings provide insights into how context influences the effectiveness of fairness-aware practices. This research aims to guide SE practitioners in selecting practices that achieve fairness with minimal performance costs, supporting the development of ethical ML systems.

Problem

• Evaluates fairness-aware practices in ML across different domains.
• Assesses cost-effectiveness of fairness improvements versus performance trade-offs.
• Guides selection of practices for ethical ML with minimal performance loss.

Innovation

• Contextual evaluation of fairness-aware practices
• Cost-effectiveness analysis of fairness improvements
• Domain-specific customization for ML fairness