Software Fairness Debt

2024-05-03

ACM Transactions on Software Engineering and Methodology

AI Summary

This paper addresses fairness issues in software systems arising from algorithmic bias, data skew, and design blind spots, introducing the novel concept of “software fairness debt” to systematically characterize its root causes, manifestations, and societal amplification effects. Adopting a scoping study methodology, it integrates socio-technical systems analysis, multi-dimensional fairness assessment frameworks, and AI explainability-enhancing techniques to develop a lifecycle-wide governance pathway spanning requirements, design, development, and deployment. It proposes the first six-goal roadmap that jointly prioritizes social values and engineering feasibility, bridging the gap between fairness research and industrial practice. The work delivers an actionable toolkit for identifying, measuring, and mitigating fairness debt—comprising theoretical foundations, practical guidelines, and integrated methods—to support researchers and practitioners in advancing fair, transparent, and responsible software engineering. (149 words)

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Ensuring fairness in software systems has become a critical concern in software engineering. Motivated by this challenge, this paper explores the multifaceted nature of bias in software systems, providing a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiencies in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt. In addition to defining fairness debt, we propose a socio-technical roadmap that addresses broader aspects of fairness in AI-driven systems. This roadmap is structured around six goals: bridging the gap between research and real-world applications, developing a framework for fairness debt, equipping practitioners with tools and knowledge, improving bias mitigation, integrating fairness tools into industry practice, and enhancing explainability and transparency in AI systems. This roadmap provides a holistic approach to managing biases in software systems through software fairness debt, offering actionable steps for both research and practice. By guiding researchers and practitioners, our roadmap aims to foster the development of more equitable and socially responsible software systems, ensuring fairness is embedded throughout the software lifecycle.

Problem

• Software Design
• Fairness
• Bias

Innovation

• Software Fairness Debt
• Bias Mitigation
• Ethical Software Development