Machine Learning, AI, Computational Linguistics, and Information Retrieval
Developing methods that allow computers to perform learned tasks autonomously, creating practical solutions for human needs.
Research Projects
Institute for Trustworthy AI in Law and Society (TRAILS)
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The TRAILS (Trustworthy AI in Law and Society) Institute, a collaboration among several universities, aims to enhance trust in AI systems. It focuses on community participation, transparent design, and best practices. Four key research thrusts address social values, technical design, socio-technical perceptions, and governance. The institute seeks to include historically marginalized communities and promote informed AI adoption.
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The TRAILS (Trustworthy AI in Law and Society) Institute, a collaboration among several universities, aims to enhance trust in AI systems. It focuses on community participation, transparent design, and best practices. Four key research thrusts address social values, technical design, socio-technical perceptions, and governance. The institute seeks to include historically marginalized communities and promote informed AI adoption.
SaTC: CORE: Medium: Learning Code(s): Community-Centered Design of Automated Content Moderation
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.
Designing AI-powered DIY Communication Tools with AAC users
Principal Investigator(s): Stephanie Valencia
Funder: Corporation
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Principal Investigator(s): Stephanie Valencia
Funder: Corporation
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Faculty
Recent News
Knowledge graph for Southside owners harmed by urban renewal, by Nick de Raet
UMD Students Studying Archival Technology & AI Make Real-World Impact
19 UMD students participating in AICollaboratory summer courses aid Ashville Reparations Commission
Tamara Clegg Named Director of UMD’s Iribe Initiative for Inclusion and Diversity in Computing
An associate professor in the College of Information, Clegg has been at UMD since 2010