The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …

The construction industry is known to be overwhelmed with resource planning, risk
management and logistic challenges which often result in design defects, project delivery …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

A key challenge in developing and deploying Machine Learning (ML) systems is
understanding their performance across a wide range of inputs. To address this challenge …

Recent advances in machine learning have stimulated widespread interest within the
Information Technology sector on integrating AI capabilities into software and services. This …

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 …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

How can we explain the predictions of a black-box model? In this paper, we use influence
functions—a classic technique from robust statistics—to trace a model's prediction through …

With the growing success of neural networks, there is a corresponding need to be able to
explain their decisions—including building confidence about how they will behave in the …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …