2.1k citations · 2.2k across the 7 of their papers we have counts for
7 papers
Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation
Nitesh Goyal, Ian Kivlichan, Rachel Rosen +1
Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-de…
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
Alyssa Lees, Vinh Q. Tran, Yi Tay +4
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that…
"You have to prove the threat is real": Understanding the needs of Female Journalists and Activists to Document and Report Online Harassment
Nitesh Goyal, Leslie Park, Lucy Vasserman
Online harassment is a major societal challenge that impacts multiple communities. Some members of community, like female journalists and activists, bear significantly higher impac…
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation
Ian D. Kivlichan, Zi Lin, Jeremiah Liu +1
Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so a…
Limitations of Pinned AUC for Measuring Unintended Bias
Daniel Borkan, Lucas Dixon, John Li +3
This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classificatio…
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen +2
Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in…