Preliminary Insights on Industry Practices for Addressing Fairness Debt

TL;DR

This paper discovered that software professionals tackle biases in AI systems by checking if the AI’s outputs match real-world conditions, ensuring it performs well for different groups of people, and investigating biases in the training data.

Abstract

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We used a qualitative research method, gathering insights from industry professionals through interviews and employing thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and issues with training data. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and provides a foundation for developing structured guidelines to manage fairness-related issues in AI systems.

Figures (1)

• Figure 1: Conceptual of Software Fairness Debt santos2024software