Conceptual modeling is an important part of information systems development and use that
involves identifying and representing relevant aspects of reality. Although the past decades …

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 …

Frontline health workers are the first and often the only access point to basic health care
services in low-and-middle income countries. However, the work and the issues frontline …

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 …

Field workers, like farmers and radiologists, play a crucial role in dataset collection for AI
models in low-resource settings. However, we know little about how field workers' expertise …

Many recent technological advances (eg ChatGPT and search engines) are possible only
because of massive amounts of user-generated data produced through user interactions …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

Data-driven approaches that form the foundation of advancements in machine learning (ML)
are powered in large part by human infrastructures that enable the collection of large …

Why healthcare cannot—and should not—become data-driven, despite the many promises
of intensified data sourcing. In contemporary healthcare, everybody seems to want more …

Using data and data technologies to support healthcare has drawn significant attention
recently. While CSCW and HCI have largely celebrated the tremendous promise of'data …