9 citations · 9 across the 3 of their papers we have counts for
3 papers
What You Read is What You Classify: Highlighting Attributions to Text and Text-Like Inputs
Daniel S. Berman, Brian Merritt, Stanley Ta +4
At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well…
CaTE Data Curation for Trustworthy AI
Mary Versa Clemens-Sewall, Christopher Cervantes, Emma Rafkin +6
This report provides practical guidance to teams designing or developing AI-enabled systems for how to promote trustworthiness during the data curation phase of development. In thi…
Interpreting Deep Neural Networks Through Variable Importance
Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman +2
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited. Unlike previously p…