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 …

This systematic literature review synthesizes the conceptualizations of ethical principles in AI
auditing literature and the knowledge contributions to the stakeholders of AI auditing. We …

AbstractScale the model, scale the data, scale the compute'is the reigning sentiment in the
world of generative AI today. While the impact of model scaling has been extensively …

The hidden costs of artificial intelligence, from natural resources and labor to privacy and
freedom What happens when artificial intelligence saturates political life and depletes the …

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 …

Proponents of generative AI tools claim they will supplement, even replace, the work of
cultural production. This raises questions about the politics of visibility: what kinds of stories …

Human-centered artificial intelligence (HCAI) seeks to shift the focus in AI development from
technology to people. However, it is not clear whether existing HCAI principles and practices …

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 …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

Machine learning (ML) depends on data to train and verify models. Very often, organizations
outsource processes related to data work (ie, generating and annotating data and …