7 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.CL2022
Democratizing Machine Learning for Interdisciplinary Scholars: Report on Organizing the NLP+CSS Online Tutorial Series
Ian Stewart, Katherine Keith
Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, M…
cs.LG2020★ 7 cited
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
Brittany Johnson, Jesse Bartola, Rico Angell +4
Modern software relies heavily on data and machine learning, and affects decisions that shape our world. Unfortunately, recent studies have shown that because of biases in data, so…