As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that give nuance to …

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 …

Deep learning models for natural language processing (NLP) are increasingly adopted and
deployed by analysts without formal training in NLP or machine learning (ML). However, the …

As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

A growing body of work shows that many problems in fairness, accountability, transparency,
and ethics in machine learning systems are rooted in decisions surrounding the data …

Human annotated data plays a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and decisions that …

There have been increasing calls for centering impacted communities–both online and
offline–in the design of the AI systems that will be deployed in their communities. However …

In citizen science, volunteers collect and share data with researchers, other volunteers, and
the public at large. Data shared in citizen science includes information on volunteer location …

The explosion of data-driven applications using Artificial Intelligence (AI) in recent years has
given rise to a variety of ethical issues regarding data collection, annotation, and processing …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …