This article expands on recent studies of machine learning or artificial intelligence (AI)
algorithms that crucially depend on benchmark datasets, often called 'ground truths.'These …

Artificial intelligence (AI) technologies are widely deployed in smartphone photography; and
prompt-based image synthesis models have rapidly become commonplace. In this paper …

As part of ongoing research bridging ethnomethodology and computer science, in this article
we offer an alternate reading of Alan Turing's 1936 paper,“On Computable Numbers” …

A growing body of work draws on feminist thinking to challenge assumptions about how
people engage with and use visualizations. This work draws on feminist values, driving …

There is a problematic tradition of dualistic and reductionist thinking in artificial intelligence
(AI) research, which is evident in AI storytelling and imaginations as well as in public …

Although considered to be fixed sequence of computational procedures, the actual nature of
algorithms emerges only in practice through its performative agency enacted within a …

Standards are put forward as important means to turn the ideals of ethical and responsible
artificial intelligence into practice. One principle targeted for standardization is transparency …

Algorithmic decision-making has permeated health and care domains (eg, automated
diagnoses, fall detection, caregiver staffing). Researchers have raised concerns about how …

Objectives Lack of trust and transparency is stressed as a challenge for clinical
implementation of artificial intelligence (AI). In breast cancer screening, AI-supported …

Over past decades, predictive algorithms have been used extensively as profiling tools in
the private sector, but today they are also increasingly entering public sector domains. This …