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

Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …

While code generation has been widely used in various software development scenarios,
the quality of the generated code is not guaranteed. This has been a particular concern in …

This chapter explores the foundational concept of robustness in Machine Learning (ML) and
its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The …

Given a discriminating neural network, the problem of fairness improvement is to
systematically reduce discrimination without significantly scarifies its performance (ie …

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 …

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 …

Deep neural network (DNN) testing approaches have grown fast in recent years to test the
correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …

Neural networks are important computational models used in the domains of artificial
intelligence and software engineering. Parameters of a neural network are obtained via …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …