Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining
momentum: the increased attention comes partly with the view that participation opens the …

In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …

Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise
toxic language which hinders their safe deployment. We investigate the extent to which …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

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 …

Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

In response to growing concerns of bias, discrimination, and unfairness perpetuated by
algorithmic systems, the datasets used to train and evaluate machine learning models have …