In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …

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 …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

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 …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Any given AI system cannot be accepted unless its trustworthiness is proven. An important
characteristic of a trustworthy AI system is the absence of algorithmic bias.'Individual …

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 …

Abstract Machine Learning has seen tremendous growth recently, which has led to a larger
adaptation of ML systems for educational assessments, credit risk, healthcare, employment …