Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

The widespread adoption of Machine Learning systems, especially in more decision-critical
applications such as criminal sentencing and bank loans, has led to increased concerns …

Fairness of machine learning (ML) software has become a major concern in the recent past.
Although recent research on testing and improving fairness have demonstrated impact on …

The emergence of Graph Neural Networks (GNNs) has greatly advanced the development
of recommendation systems. Recently, many researchers have leveraged GNN-based …

Existing research mostly improves the fairness of Machine Learning (ML) software regarding
a single protected attribute at a time, but this is unrealistic given that many users have …

This paper conducts fairness testing of automated pedestrian detection, a crucial but under-
explored issue in autonomous driving systems. We evaluate eight state-of-the-art deep …

Background: The development of AI-enabled software heavily depends on AI model
documentation, such as model cards, due to different domain expertise between software …

This paper investigates neuron dropout as a post-processing bias mitigation method for
deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in …

This paper conducts fairness testing on automated pedestrian detection, a crucial but under-
explored issue in autonomous driving systems. We evaluate eight widely-studied pedestrian …