activity
20162023
most citedMultimodal Social Media Analysis for Gang Violence Prevention

11 citations · 28 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2023★ 4 cited

Evaluation of African American Language Bias in Natural Language Generation

Nicholas Deas, Jessi Grieser, Shana Kleiner +3

We evaluate how well LLMs understand African American Language (AAL) in comparison to their performance on White Mainstream English (WME), the encouraged "standard" form of English…

cs.CL2022★ 3 cited

SafeText: A Benchmark for Exploring Physical Safety in Language Models

Sharon Levy, Emily Allaway, Melanie Subbiah +4

Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe. One such typ…

cs.AI2022★ 2 cited

Mitigating Covertly Unsafe Text within Natural Language Systems

Alex Mei, Anisha Kabir, Sharon Levy +7

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threa…

cs.CL2019

Detecting and Reducing Bias in a High Stakes Domain

Ruiqi Zhong, Yanda Chen, Desmond Patton +2

Gang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep lear…

cs.AI2018

An Overview of Computational Approaches for Interpretation Analysis

Philipp Blandfort, Jörn Hees, Desmond U. Patton

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an i…

cs.CL2018★ 1 cited

Detecting Gang-Involved Escalation on Social Media Using Context

Serina Chang, Ruiqi Zhong, Ethan Adams +6

Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience…