The prototypical NLP experiment trains a standard architecture on labeled English data and
optimizes for accuracy, without accounting for other dimensions such as fairness …

The increasing use of machine learning in high-stakes domains–where people's livelihoods
are impacted–creates an urgent need for interpretable, fair, and highly accurate algorithms …

Fair classification and fair representation learning are two important problems in supervised
and unsupervised fair machine learning, respectively. Fair classification asks for a classifier …

In machine learning, training data often capture the behaviour of multiple subgroups of some
underlying human population. This behaviour can often be modelled as observations of an …

We extend Langdon Winner's idea that artifacts have politics into the realm of mathematics.
To do so, we first provide a list of examples showing the existence of mathematical artifacts …

This work presents CounterNet, a novel end-to-end learning framework which integrates
Machine Learning (ML) model training and the generation of corresponding counterfactual …

Predictive analytics has been widely used in various domains, including education, to inform
decision-making and improve outcomes. However, many predictive models are proprietary …

Interpretability has recently gained attention in the field of machine learning, for it is crucial
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …

Machine learning techniques are increasingly used for high-stakes decision-making, such
as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure …

The analysis of discrimination has long interested economists and lawyers. In recent years,
the literature in computer science and machine learning has become interested in the …