Previous research has investigated gender and its implications for HCI. We consider
inclusive design of technology whatever the gender of its users of particular importance. This …

The purpose of this paper is to delineate the research challenges of human—machine
collaboration in risky decision-making. Technological advances in machine intelligence …

Advances in artificial intelligence, sensors and big data management have far-reaching
societal impacts. As these systems augment our everyday lives, it becomes increasing-ly …

Convolutional neural networks (CNNs) offer great machine learning performance over a
range of applications, but their operation is hard to interpret, even for experts. Various …

The successful deployment of artificial intelligence (AI) in many domains from healthcare to
hiring requires their responsible use, particularly in model explanations and privacy …

Ensuring fairness in artificial intelligence (AI) is important to counteract bias and
discrimination in far-reaching applications. Recent work has started to investigate how …

Everyday systems and devices in the home are becoming smarter. In order to better
understand the challenges of deploying an intelligent system in the home, we studied the …

What does a user need to know to productively work with an intelligent agent? Intelligent
agents and recommender systems are gaining widespread use, potentially creating a need …

Debugging a machine learning model is hard since the bug usually involves the training
data and the learning process. This becomes even harder for an opaque deep learning …

Intelligibility can help expose the inner workings and inputs of context-aware applications
that tend to be opaque to users due to their implicit sensing and actions. However, users …