Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might
improve research. Why are AI tools so attractive and what are the risks of implementing them …

Computational social science is more than just large repositories of digital data and the
computational methods needed to construct and analyse them. It also represents a …

Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …

The use of black box algorithms in medicine has raised scholarly concerns due to their
opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and …

Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

With vast potentials in improving operations and stimulating growth, digital transformation
has aroused much attention from firms across the world. However, the high costs associated …

Post-hoc explanations of machine learning models are crucial for people to understand and
act on algorithmic predictions. An intriguing class of explanations is through counterfactuals …

A central question for information systems (IS) researchers and practitioners is if, and how,
big data can help attain a competitive advantage. To address this question, this study draws …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …