A recent wave of research has attempted to define fairness quantitatively. In particular, this
work has explored what fairness might mean in the context of decisions based on the …

One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

The potential for machine learning (ML) systems to amplify social inequities and unfairness
is receiving increasing popular and academic attention. A surge of recent work has focused …

Increasingly, software is making autonomous decisions in case of criminal sentencing,
approving credit cards, hiring employees, and so on. Some of these decisions show bias …

We define perceived diversity as the diversity factors that individuals are born with.
Perceived diversity in Software Engineering has been recognized as a high-value team …

A recent flurry of research activity has attempted to quantitatively define" fairness" for
decisions based on statistical and machine learning (ML) predictions. The rapid growth of …

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

Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic
critical decision support software systems. DNNs are exceptionally good at finding min-imal …