I'm pleased to share that we had two papers accepted at the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2024).

Our first paper, "From Literature to Practice: Exploring Fairness Testing Tools for the Software Industry Adoption," addresses the increasing need for fairness in AI-powered software systems. In this study, we evaluated available fairness testing tools in the literature and explored their practical application in software development. Our findings highlight the necessity for fairness testing tools that are more aligned with industry requirements, particularly in terms of user-friendliness, comprehensiveness, and configurability.

Our second paper, "Preliminary Insights on Industry Practices for Addressing Fairness Debt," examines how industry professionals identify and manage biases in AI systems. Our findings indicate that practitioners detect biases by identifying discrepancies between model outputs and real-world conditions, inconsistencies in performance across demographics, and biases arising from training data and algorithmic behavior during testing. To mitigate these biases, practices such as enhancing data management, regular model adjustments, effective crisis management, improving team diversity, and relying on ethical and bias analysis are commonly employed.