Academics and companies increasingly draw on large datasets to understand the social
world, and name-based demographic ascription tools are widespread for imputing …

Racial disparity in academia is a widely acknowledged problem. The quantitative
understanding of racial-based systemic inequalities is an important step towards a more …

While rich medical, behavioral, and socio-demographic data are key to modern data-driven
research, their collection and use raise legitimate privacy concerns. Anonymizing datasets …

There is a growing body of work that proposes methods for mitigating bias in machine
learning systems. These methods typically rely on access to protected attributes such as …

Prediction of individuals' race and ethnicity plays an important role in studies of racial
disparity. Bayesian Improved Surname Geocoding (BISG), which relies on detailed census …

This article uses a recent first name list to develop an improvement to an existing Bayesian
classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which …

Pre-trained language models encode undesirable social biases, which are further
exacerbated in downstream use. To this end, we propose MABEL (a Method for Attenuating …

The measurement of bias in machine learning often focuses on model performance across
identity subgroups (such as man and woman) with respect to groundtruth labels. However …

A growing body of work shows that many problems in fairness, accountability, transparency,
and ethics in machine learning systems are rooted in decisions surrounding the data …

A new strand of literature aims at building the most comprehensive and accurate database
of notable individuals. We collect a massive amount of data from various editions of …