Biomedical scientific literature is growing at a very rapid pace, which makes increasingly
difficult for human experts to spot the most relevant results hidden in the papers …

To address various business challenges, organisations are increasingly employing artificial
intelligence (AI) to analyse vast amounts of data. One application involves consolidating …

Data in general encodes human biases by default; being aware of this is a good start, and
the research around how to handle it is ongoing. The term 'bias' is extensively used in …

Objective Machine learning is used to understand and track influenza-related content on
social media. Because these systems are used at scale, they have the potential to adversely …

Data-driven analyses of biases in historical texts can help illuminate the origin and
development of biases prevailing in modern society. However, digitised historical …

This research introduces the Multilevel Embedding Association Test (ML-EAT), a method
designed for interpretable and transparent measurement of intrinsic bias in language …

Social bias in language-towards genders, ethnicities, ages, and other social groups-poses a
problem with ethical impact for many NLP applications. Recent research has shown that …

Objective To analyze gender bias in clinical trials, to design an algorithm that mitigates the
effects of biases of gender representation on natural-language (NLP) systems trained on text …

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 …

News articles both shape and reflect public opinion across the political spectrum. Analyzing
them for social bias can thus provide valuable insights, such as prevailing stereotypes in …