RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization

no code implementations • 8 Feb 2021 • Austin P Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Duen Horng Chau, Diyi Yang

With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments.

RECAST: Interactive Auditing of Automatic Toxicity Detection Models

no code implementations • 7 Jan 2020 • Austin P. Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Diyi Yang, Duen Horng Chau

As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments.

Adversarial Robustness Fairness

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

1 code implementation • 10 Apr 2019 • Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau

We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models.

BIG-bench Machine Learning Fairness +1