Allocating resources to individuals in a fair manner has been a topic of interest since ancient
times, with most of the early mathematical work on the problem focusing on resources that …

Recommender systems provide personalized information access to users of Internet
services from social networks to e-commerce to media and entertainment. As is appropriate …

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with
the preferences of a prototypical user. This work assumes that human preferences are static …

With increased power and prevalence of AI systems, it is ever more critical that AI systems
are designed to serve all, ie, people with diverse values and perspectives. However …

Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Globally, there has been a recent surge in 'citizens' assemblies', which are a form of civic
participation in which a panel of randomly selected constituents contributes to questions of …

The maximum Nash welfare (MNW) solution—which selects an allocation that maximizes
the product of utilities—is known to provide outstanding fairness guarantees when allocating …

Allocating resources to individuals in a fair manner has been a topic of interest since the
ancient times, with most of the early rigorous mathematical work on the problem focusing on …

The goal of fair division is to distribute resources among competing players in a “fair" way.
Envy-freeness is the most extensively studied fairness notion in fair division. Envy-free …