Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

The extensive adoption of business analytics (BA) has brought financial gains and
increased efficiencies. However, these advances have simultaneously drawn attention to …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Human-machine complementarity is important when neither the algorithm nor the human
yield dominant performance across all instances in a given domain. Most research on …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …

Human-AI complementarity is important when neither the algorithm nor the human yields
dominant performance across all instances in a given context. Recent work that explored …

Human-AI collaboration has the potential to transform various domains by leveraging the
complementary strengths of human experts and Artificial Intelligence (AI) systems. However …

Biases can arise and be introduced during each phase of a supervised learning pipeline,
eventually leading to harm. Within the task of automatic abusive language detection, this …

We introduce BRIDGET, a novel human-in-the-loop system for hybrid decision-making,
aiding the user to label records from an un-labeled dataset, attempting to``bridge the …

Using artificial intelligence (AI) systems for informing high-stakes decisions has become
increasingly pervasive in a variety of domains, including but not limited to hiring, lending, or …