In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

Recent works leverage LLMs to roleplay realistic social scenarios, aiding novices in
practicing their social skills. However, simulating sensitive interactions, such as in mental …

To investigate the well-observed racial disparities in computer vision systems that analyze
images of humans, researchers have turned to skin tone as a more objective annotation …

When groups of people are tasked with making a judgment, the issue of uncertainty often
arises. Existing methods to reduce uncertainty typically focus on iteratively improving …

Data annotation interfaces predominantly leverage ground truth labels to guide annotators
toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain …

Human-annotated labels and explanations are critical for training explainable NLP models.
However, unlike human-annotated labels whose quality is easier to calibrate (eg, with a …

Case studies commonly form the pedagogical backbone in law, ethics, and many other
domains that face complex and ambiguous societal questions informed by human values …

As machine learning methods become more powerful and capture more nuances of human
behavior, biases in the dataset can shape what the model learns and is evaluated on. This …

The use of machine learning (ML)-based language models (LMs) to monitor content online
is on the rise. For toxic text identification, task-specific fine-tuning of these models are …

The aggregation of multiple opinions plays a crucial role in decision-making, such as in
hiring and loan review, and in labeling data for supervised learning. Although majority voting …