We consider the problem of generating rankings that are fair towards both users and item
producers in recommender systems. We address both usual recommendation (eg, of music …

Decision-making systems increasingly orchestrate our world: how to intervene on the
algorithmic components to build fair and equitable systems is therefore a question of utmost …

Massively multilingual pre-trained language models, such as mBERT and XLM-RoBERTa,
have received significant attention in the recent NLP literature for their excellent capability …

Randomized experiments have been the gold standard for assessing the effectiveness of a
treatment, policy, or intervention, spanning various fields, including social sciences …

While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

Rankings have become the primary interface in two-sided online markets. Many have noted
that the rankings not only affect the satisfaction of the users (eg, customers, listeners …

Applying standard machine learning approaches for classification can produce unequal
results across different demographic groups. When then used in real-world settings, these …

In this work, we study fairness in centroid clustering. In this problem, k cluster centers must
be placed given n points in a metric space, and the cost to each point is its distance to the …

Prior probability shift is a phenomenon where the training and test datasets differ structurally
within population subgroups. This phenomenon can be observed in the yearly records of …

We incorporate group fairness into the algorithmic centroid clustering problem, where $ k $
centers are to be located to serve $ n $ agents distributed in a metric space. We refine the …