3 citations · 6 across the 5 of their papers we have counts for
5 papers
Designing Around Stigma: Human-Centered LLMs for Menstrual Health
Amna Shahnawaz, Ayesha Shafique, Ding Wang +1
Menstrual health education (MHE) in Pakistan is constrained by cultural taboos and inadequate formal curricula, leaving women with few trusted resources to lean on. In response to…
The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
Sheza Munir, Benjamin Mah, Krisha Kalsi +5
In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" paradigm rests on a positivisti…
Socially Responsible Data for Large Multilingual Language Models
Andrew Smart, Ben Hutchinson, Lameck Mbangula Amugongo +12
Large Language Models (LLMs) have rapidly increased in size and apparent capabilities in the last three years, but their training data is largely English text. There is growing int…
The Problems with Proxies: Making Data Work Visible through Requester Practices
Annabel Rothschild, Ding Wang, Niveditha Jayakumar Vilvanathan +3
Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and an…
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Andrew Smart, Ding Wang, Ellis Monk +4
Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness,…