AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

Despite impressive performance in many benchmark datasets, AI models can still make
mistakes, especially among out-of-distribution examples. It remains an open question how …

People consider recommendations from AI systems in diverse domains ranging from
recognizing tumors in medical images to deciding which shoes look cute with an outfit …

Chatbot-based symptom checker (CSC) apps have become increasingly popular in
healthcare. These apps engage users in human-like conversations and offer possible …

CHI papers researching healthcare human-computer interaction (HCI) are increasingly
reporting the use of “thematic analysis”(TA). TA refers to a range of flexible and evolving …

Machine learning models need to provide contrastive explanations, since people often seek
to understand why a puzzling prediction occurred instead of some expected outcome …

While a vast collection of explainable AI (XAI) algorithms has been developed in recent
years, they have been criticized for significant gaps with how humans produce and consume …

Explainability (XAI) has matured in recent years to provide more human-centered
explanations of AI-based decision systems. While static explanations remain predominant …