After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …

Virtual reality (VR) researchers struggle to conduct remote studies. Previous work has
focused on working around limitations imposed by traditional crowdsourcing methods …

The topic of algorithmic fairness is of increasing importance to the Human–Computer
Interaction research community following accumulating concerns regarding the use and …

In this paper, we present DendroMap, a novel approach to interactively exploring large-
scale image datasets for machine learning (ML). ML practitioners often explore image …

Human labeling of training data is often a time-consuming, expensive part of machine
learning. In this paper, we study" batch labeling", an AI-assisted UX paradigm, that aids data …

Machine Learning models can output confident but incorrect predictions. To address this
problem, ML researchers use various techniques to reliably estimate ML uncertainty, usually …

Automated sound recognition tools can be a useful complement to d/Deaf and hard of
hearing (DHH) people's typical communication and environmental awareness strategies …

Current activity tracking technologies are largely trained on younger adults' data, which can
lead to solutions that are not well-suited for older adults. To build activity trackers for older …

Iteratively building and testing machine learning models can help children develop
creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore …

While human-centered approaches to machine learning explore various human roles within
the interaction loop, the notion of Interactive Machine Teaching (IMT) emerged with a focus …