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 …

Fairness has been a critical issue that affects the adoption of deep learning models in real
practice. To improve model fairness, many existing methods have been proposed and …

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 …

It has been recently remarked that focusing only on accuracy in searching for optimal
Machine Learning models amplifies biases contained in the data, leading to unfair …

Deep Learning (DL) techniques are increasingly being incorporated in critical software
systems today. DL software is buggy too. Recent work in SE has characterized these bugs …

The advancement of machine learning (ML) models has led to the development of ML-
based approaches to improve numerous software engineering tasks in software …

Deep learning models are trained with certain assumptions about the data during the
development stage and then used for prediction in the deployment stage. It is important to …

Fairness has been a critical issue that affects the adoption of deep learning models in real
practice. To improve model fairness, many existing methods have been proposed and …

Abstract As reliance on Machine Learning (ML) systems in real-world decision-making
processes grows, ensuring these systems are free of bias against sensitive demographic …

Intersectional fairness is a critical requirement for Machine Learning (ML) software,
demanding fairness across subgroups defined by multiple protected attributes. This paper …