The Less Discriminatory Alternative is a key provision of the disparate impact doctrine in the
United States. In fair lending, this provision mandates that lenders must adopt models that …

Machine learning (ML) is widely used to moderate online content. Despite its scalability
relative to human moderation, the use of ML introduces unique challenges to content …

The generalization error of a classifier is related to the complexity of the set of functions
among which the classifier is chosen. We study a family of low-complexity classifiers …

We introduce Natural Learning (NL), a novel algorithm that elevates the explainability and
interpretability of machine learning to an extreme level. NL simplifies decisions into intuitive …

Given a classification problem and a family of classifiers, the Rashomon ratio measures the
proportion of classifiers that yield less than a given loss. Previous work has explored the …

Abstract Machine learning has been increasingly deployed for myriad high-stakes decisions
that deeply impact people's lives. This is concerning, because not every model can be …

In machine learning, model multiplicity is the existence of multiple models that perform
equally well for a given prediction task (also known as the" Rashomon effect"). The set of …

For high-stakes decision domains, such as healthcare, lending, and criminal justice, the
predictions of deployed models can have a huge impact on human lives. The understanding …

Recent research in supervised learning has demonstrated that noise in data generation
processes leads to the existence of accurate and simpler/interpretable machine learning …

Abstract The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that
there exist many equally good predictive models for the same dataset. This phenomenon …