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 …

Human participants play a central role in the development of modern artificial intelligence
(AI) technology, in psychological science, and in user research. Recent advances in …

A long-standing challenge in develo** machine learning approaches has been the lack of
high-quality labeled data. Recently models trained with purely synthetic data here termed …

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

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Protein language models (pLMs) trained on large protein sequence databases have been
used to understand disease and design novel proteins. In design tasks, the likelihood of a …

As new forms of data capture emerge to power new AI applications, questions abound about
the ethical implications of these data collection practices. In this paper, we present clinicians' …

As training datasets become increasingly drawn from unstructured, uncontrolled
environments such as the web, researchers and industry practitioners have increasingly …

We propose simple active sampling and reweighting strategies for optimizing min-max
fairness that can be applied to any classification or regression model learned via loss …