Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

As AI technology is rapidly progressing in capability and being adopted more widely across
society, it is more important than ever to understand the potential risks AI may pose and how …

A growing ecosystem of large, open-source foundation models has reduced the labeled
data and technical expertise necessary to apply machine learning to many new problems …

Studies of dataset development in machine learning call for greater attention to the data
practices that make model development possible and shape its outcomes. Many argue that …

AI alignment refers to models acting towards human-intended goals, preferences, or ethical
principles. Analyzing the similarity between models and humans can be a proxy measure for …

Data curation is a field with origins in librarianship and archives, whose scholarship and
thinking on data issues go back centuries, if not millennia. The field of machine learning is …

High-quality data is crucial for the success of machine learning, but labeling large datasets
is often a time-consuming and costly process. While semi-supervised learning can help …

Short-video platforms show an increasing impact on people's daily lives nowadays, with
billions of active users spending plenty of time each day. The interactions between users …

AI alignment refers to models acting towards human-intended goals, preferences, or ethical
principles. Given that most large-scale deep learning models act as black boxes and cannot …