Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

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

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 …

In recent years, many incidents have been reported where machine learning models
exhibited discrimination among people based on race, sex, age, etc. 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 …

Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …

Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

Deep Neural Networks (DNNs) are becoming an integral part of most software systems.
Previous work has shown that DNNs have bugs. Unfortunately, existing debugging …

The increasingly wide uptake of Machine Learning (ML) has raised the significance of the
problem of tackling bias (ie, unfairness), making it a primary software engineering concern …