Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

Stories are an important indicator of our vision of the future. In the case of artificial
intelligence (AI), dominant stories are polarized between notions of threat and myopic …

There has been a great deal of scholarly attention on issues of identity-related bias in
machine learning. Much of this attention has focused on data and data workers, workers …

Purpose The present study undertakes an extensive review of the causes of service failures
in artificial intelligence (AI) technology literature. Design/methodology/approach A hybrid …

In this article, I argue that devoting analytical attention to the natively technical concept of
“the stack” can help re-sensitize studies of algorithms in Science and Technology Studies …

Given the complexity of teams involved in creating AI-based systems, how can we
understand who should be held accountable when they fail? This paper reports findings …

Abstract Design failure modes, effects, and criticality analysis (d-FMECA) 2 is a bottom-up,
semi-quantitative risk assessment approach that is used by reliability engineers across all …

Artificial Intelligence (AI) has been described as a great hope but also a threat to fulfilling the
social goals of sustainable development, where one critical aspect is providing society with …

The scale and complexity of the data and algorithms used in artificial intelligence (AI)‐based
systems present significant challenges for anticipating their ethical, legal, and policy …

AI is a rapidly advancing technology with the potential to hugely enrich and dramatically
transform many aspects of today's society. As well as being the focus of much excitement …