Purpose Gender bias in artificial intelligence (AI) should be solved as a priority before AI
algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem …

The interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human
understanding of black-box machine learning models through explanation-generating …

Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks
ranging from online comment toxicity to misinformation detection to medical diagnosis …

The spread of AI-embedded systems involved in human decision making makes studying
human trust in these systems critical. However, empirically investigating trust is challenging …

Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for
AI-powered systems remains challenging. UX designers face hurdles understanding AI …

Recent years have seen the development of many open-source ML fairness toolkits aimed
at hel** ML practitioners assess and address unfairness in their systems. However, there …

The present case study examines the product landscape of current AI-empowered co-
creative tools. Specifically, I review literature in both creativity and HCI research and …

Research exploring how to support decision-making has often used machine learning to
automate or assist human decisions. We take an alternative approach for improving decision …

The interpretability or explainability of AI systems (XAI) has been a topic gaining renewed
attention in recent years across AI and HCI communities. Recent work has drawn attention to …