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 …

Scholars in several fields, including quantitative methodologists, legal scholars, and
theoretically oriented criminologists, have launched robust debates about the fairness of …

Public agencies aiming to enforce environmental regulation have limited resources to
achieve their objectives. We demonstrate how machine-learning methods can inform the …

The prosperous trend of deploying deep neural network (DNN) models to diverse hardware
platforms has boosted the development of deep learning (DL) compilers. DL compilers take …

Although researchers increasingly adopt machine learning to model travel behavior, they
predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in …

Decisions made by various artificial intelligence (AI) systems greatly influence our day-to-
day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair …

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 …

We provide an overview of the state-of-the-art in fairnessaware machine learning and
examine a wide variety of research articles in the area. We survey different fairness notions …

Algorithmic bias arises in machine learning when models that may have reasonable overall
accuracy are biased in favor of 'good'outcomes for one side of a sensitive category, for …

Algorithmic bias due to underestimation refers to situations where an algorithm under-
predicts desirable outcomes for a protected minority. In this paper we show how this can be …