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 …

Data physicalization is a rich and vast research area that studies the use of physical artifacts
to convey data. It overlaps with a number of research areas including information …

With the rise of big data, there has been an increasing need for practitioners in this space
and an increasing opportunity for researchers to understand their workflows and design new …

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to
low-resourced languages has not yet been adequately solved." Low-resourced"-ness is a …

A proposal to redefine design in a way that not only challenges the field's dominant
paradigms but also changes the practice of design itself. In Critical Fabulations, Daniela …

Diversity in datasets is a key component to building responsible AI/ML. Despite this
recognition, we know little about the diversity among the annotators involved in data …

We introduce embedded data representations, the use of visual and physical
representations of data that are deeply integrated with the physical spaces, objects, and …

We present a review of 80 papers representing efforts to support participation in democratic
decision-making mostly related to local or national governments. The papers were …

Citizen sensing is an approach that develops and uses lightweight technologies with local
communities to collect, share and act upon data. In doing so it enables them to become …

The current economic crisis has thrown the relationship between citizens, communities and
the state into sharp relief. Digital Civics is an emerging cross-disciplinary area of research …