Detecting, measuring and mitigating various measures of unfairness are core aims of
algorithmic fairness research. However, the most prominent approaches require access to …

Uncovering the world's ethnic inequalities is hampered by a lack of ethnicity-annotated
datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people's …

Large-scale microdata on group identity are critical for studies on identity politics and
violence but remain largely unavailable for developing countries. We use personal names to …

Automatically recognising and extracting the reasoning expressed in natural language text
is extremely demanding and only very recently has there been significant headway. While …

Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in
many large databases impedes effective population research and interventions. Automated …

Participation on social media platforms has many benefits but also poses substantial threats.
Users often face an unintended loss of privacy, are bombarded with mis-/disinformation, or …

Enriching datasets with demographic information, such as gender, race, and age from
names, is a critical task in fields like healthcare, public policy, and social sciences. Such …

Bayesian Improved Surname Geocoding (BISG) is the most popular method for proxying
race/ethnicity in voter registration files that do not contain it. This paper benchmarks BISG …

Name-based ethnicity classification is the task of predicting ethnicity from a name. Ethnicity
classification can be a key tool for assessing the fairness of algorithms, demographic …

The study of surnames as both linguistic and geographical markers of the past has proven
valuable in several research fields spanning from biology and genetics to demography and …