(Gates & Hillman Centers (GHC) 6115, Carnegie Mellon University, April 18, 2025)

Speaker

Anjalie Field
Anjalie Field
JHU
Anjalie Field is an Assistant Professor in the Computer Science Department at Johns Hopkins University. She is also affiliated with the Center for Language and Speech Processing (CLSP) and the new Data Science and AI Institute. Her research focuses on the ethics and social science aspects of natural language processing, which includes developing models to address societal issues like discrimination and propaganda, as well as critically assessing and improving ethics in AI pipelines. Her work has been published in NLP and interdisciplinary venues, like ACL and PNAS, and in 2024 she was named an AI2050 Early Career Fellow by Schmidt Futures. Prior to joining JHU, she was a postdoctoral researcher at Stanford, and she completed her PhD at the Language Technologies Institute at Carnegie Mellon University.

Talk Description

Title: Fairness and Privacy in High-Stakes NLP

Abstract: Practitioners are increasingly using algorithmic tools in high-stakes settings, like healthcare, social services, policing, and education with particular recent interest in natural language processing (NLP). These domains raise a number of challenges, including preserving data privacy, ensuring model reliability, and developing approaches that can mitigate, rather than exacerbate historical bias. In this talk, I will discuss our recent work investigating risks of racial bias in NLP child protective services and ways we aim to better preserve privacy for these types of audits in the future. Time permitting, I will also discuss, our development of speech processing tools for policy body camera footage, which aims to improve police accountability. Both domains involve challenges in working with messy minimally processed data containing sensitive information and domain-specific language. This work emphasizes how NLP has potential to advance social justice goals, like police accountability, but also risks causing direct harm by perpetuating bias, reducing privacy and increasing power imbalances.

Time: 3:30pm - 4:30pm