Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …

What does it mean for a generative AI model to be explainable? The emergent discipline of
explainable AI (XAI) has made great strides in helping people understand discriminative …

Various tools and practices have been developed to support practitioners in identifying,
assessing, and mitigating fairness-related harms caused by AI systems. However, prior …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

This workshop applies human centered themes to a new and powerful technology,
generative artificial intelligence (AI). Unlike AI systems that produce decisions or …

Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

Explainability of AI systems is critical for users to take informed actions and hold systems
accountable. While" opening the opaque box" is important, understanding who opens the …

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

In conventional software development, user experience (UX) designers and engineers
collaborate through separation of concerns (SoC): designers create human interface …