Machine learning models that convert user-written text descriptions into images are now
widely available online and used by millions of users to generate millions of images a day …

Recent work has aimed to capture nuances of human behavior by using LLMs to simulate
responses from particular demographics in settings like social science experiments and …

Preface “14 litres d'encre de chine, 30 pinceaux, 62 crayons à mine grasse, 1 crayon à mine
dure, 27 gommes à effacer, 38 kilos de papier, 16 rubans de machine à écrire, 2 machines à …

Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer
learning'simple'features over more'complex'ones, even when the latter may be more …

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts
around bias mitigation strategies. A vast majority of the proposed approaches fall under one …

Recent philosophical work has explored how the social identity of knowers influences how
their contributions are received, assessed, and credited. However, a critical gap remains …

Machine learning has grown in popularity to help assign resources and make decisions
about users, which can result in discrimination. This includes hiring markets, where …

We return here to the usual protected, or sensitive, variables that can lead to discrimination
in insurance. We mention direct discrimination, with race and ethnic origin, gender and sex …

Gender bias is one of the types of bias studied in fair machine learning (ML), which seeks
equity in the predictions made by ML models. Bias mitigation is often based on protecting …

In the era of big data, there is a tremendous amount of information on the Internet, but
people can only access part of it. Information obtained through only limited channels can …