Abstract Machines powered by artificial intelligence increasingly mediate our social, cultural,
economic and political interactions. Understanding the behaviour of artificial intelligence …

This paper reviews recent studies in understanding neural-network representations and
learning neural networks with interpretable/disentangled middle-layer representations …

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

Large language models (LLMs) are increasingly becoming all-powerful and pervasive via
deployment in sociotechnical systems. Yet these language models, be it for classification or …

The potential for machine learning (ML) systems to amplify social inequities and unfairness
is receiving increasing popular and academic attention. A surge of recent work has focused …

Decisions made by human-AI teams (eg., AI-advised humans) are increasingly common in
high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

We address a relatively under-explored aspect of human-computer interaction: people's
abilities to understand the relationship between a machine learning model's stated …

This paper proposes a method to modify a traditional convolutional neural network (CNN)
into an interpretable CNN, in order to clarify knowledge representations in high conv-layers …