The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Abstract Machine learning and deep learning models are commonly developed using
programming languages such as Python, C++, or R and deployed as web apps delivered …

Interfaces for machine learning (ML), information and visualizations about models or data,
can help practitioners build robust and responsible ML systems. Despite their benefits …

Machine learning models with high accuracy on test data can still produce systematic
failures, such as harmful biases and safety issues, when deployed in the real world. To …

Machine learning practitioners often end up tunneling on low-level technical details like
model architectures and performance metrics. Could early model development instead focus …

Interactive Machine Teaching (IMT) systems allow non-experts to easily create Machine
Learning (ML) models. However, existing vision-based IMT systems either ignore …

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 …

This paper combines design, machine learning and social computing to explore generative
deep learning as both tool and probe for respiratory care. We first present GANspire, a deep …

Human-centered AI aims to bridge the gap between machine decision-making and human
understanding. However, even for classification tasks where deep neural networks have …

One of the challenges of technology-assisted motor learning is how to adapt practice to
facilitate learning. Random practice has been shown to promote long-term learning …