Preliminary Insights on Industry Practices for Addressing Fairness Debt

TL;DR

This paper investigates how software professionals detect and mitigate biases in AI systems, highlighting practical strategies and initial insights into managing fairness debt in the industry.

Contribution

It provides qualitative insights into industry practices for addressing fairness debt and suggests foundational guidelines for bias mitigation in AI.

Findings

Biases are identified through output discrepancies and demographic issues.

Practitioners use data management and model adjustments to mitigate bias.

Strategies include team diversity and ethical analysis.

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.