In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Large-scale pre-training has recently emerged as a technique for creating capable, general-
purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many …

Although the fairness community has recognized the importance of data, researchers in the
area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Abstract Machine learning models are known to perpetuate and even amplify the biases
present in the data. However, these data biases frequently do not become apparent until …

We formalize predictive optimization, a category of decision-making algorithms that use
machine learning (ML) to predict future outcomes of interest about individuals. For example …

Robustness to distribution shift has become a growing concern for text and image models as
they transition from research subjects to deployment in the real world. However, high-quality …

As post hoc explanation methods are increasingly being leveraged to explain complex
models in high-stakes settings, it becomes critical to ensure that the quality of the resulting …