Democratizing Machine Learning for Interdisciplinary Scholars: Report on Organizing the NLP+CSS Online Tutorial Series

no code implementations • 29 Nov 2022 • 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.

Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models

1 code implementation • 17 Dec 2020 • Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J. Giguere, Yuriy Brun

To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.

BIG-bench Machine Learning Fairness

Modeling Financial Analysts' Decision Making via the Pragmatics and Semantics of Earnings Calls

no code implementations • ACL 2019 • Katherine Keith, Am Stent, a

Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts.

Decision Making

Uncertainty-aware generative models for inferring document class prevalence

1 code implementation • EMNLP 2018 • Katherine Keith, Brendan O{'}Connor

Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group{---}for example, the proportion of a document collection with positive sentiment.

Bayesian Inference Epidemiology +1