The adoption of artificial intelligence (AI) and machine learning (ML) in risk-sensitive
environments is still in its infancy because it lacks a systematic framework for reasoning …

Background A growing amount of health research uses social media data. Those critical of
social media research often cite that it may be unrepresentative of the population; however …

Dialect differences caused by regional, social, and economic factors cause performance
discrepancies for many groups of language technology users. Inclusive and equitable …

Intimate partner violence (IPV) is a preventable public health problem that affects millions of
people worldwide. Approximately one in four women are estimated to be or have been …

Background The lack of nonparametric statistical tests for confounding bias significantly
hampers the development of robust, valid, and generalizable predictive models in many …

AI bias has been reported in many areas, including business (Manyika, 2019; Manyika et al.,
2019), social media (Nouri, 2021), the economy (Omowole, 2021), politics (Kumawat, 2020) …

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these
language technologies do not accrue evenly to all users, and indeed they can be harmful; …

A bias in health research to favor understanding diseases as they present in men can have
a grave impact on the health of women. This paper reports on a conceptual review of the …

Text classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless,
many studies show that classifiers are biased regarding different languages and dialects …

This paper explores current and hypothetical implementations of machine learning in the
creation and marketing of cultural commodities such as music. Building on Adorno and …