After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …

The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns
regarding explainability. Recent studies have discussed the emerging demand for …

Recently, Artificial Intelligence (AI) has been used to enable efficient decision-making in
managerial and organizational contexts, ranging from employment to dismissal. However, to …

Quality aspects such as ethics, fairness, and transparency have been proven to be essential
for trustworthy software systems. Explainability has been identified not only as a means to …

Ground-truth labeling is an important activity in machine learning. Many studies have
examined how crowdworkers apply labels to records in machine learning datasets …

Design research is important for understanding and interrogating how emerging
technologies shape human experience. However, design research with Machine Learning …

Human labeling of training data is often a time-consuming, expensive part of machine
learning. In this paper, we study" batch labeling", an AI-assisted UX paradigm, that aids data …

Artificial intelligence (AI) is increasingly being deployed in high-stakes domains, such as
disaster relief and radiology, to aid practitioners during the decision-making process …

Explainable AI has the potential to support more interactive and fluid co-creative AI systems
which can creatively collaborate with people. To do this, creative AI models need to be …

Predicting forced displacement is an important undertaking of many humanitarian aid
agencies, which must anticipate flows in advance in order to provide vulnerable refugees …