Research Projects
Filtered by: Information Justice, Human Rights, and Technology Ethics
CAREER: Self-Directed Human-LLM Coordination for Language Learning and Information Seeking
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Collaborative Research: ER2: Developing Educational Resources for the Ethical Use of Pervasive Data
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Collaborative Research: ER2: The development of research ethics governance projects in computer science
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics
This project characterizes and evaluates historical, ongoing, and emerging ethics governance projects within computer science. By creating a recent history of computing governance during this active period of questioning, the project will appraise and evaluate current efforts, and recommend best practices for computing research governance.
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics
This project characterizes and evaluates historical, ongoing, and emerging ethics governance projects within computer science. By creating a recent history of computing governance during this active period of questioning, the project will appraise and evaluate current efforts, and recommend best practices for computing research governance.
Computational Treatments to re-member the Legacy of Slavery (CT-LoS)
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Computer and Information Science and Engineering Graduate Fellowships (CSGrad4US) – Micah Morgan
Principal Investigator(s): Sheena Erete
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics Library and Information Science Youth Experience, Learning, and Digital Practices
The CSGrad4US Fellowship Program supports domestic bachelor’s degree holders returning to academia by providing mentoring and funding to pursue PhDs in computing. Through this cooperative agreement, the Computing Research Association will manage recruitment, applications, mentoring, and program evaluation to expand diversity in CISE fields.
Principal Investigator(s): Sheena Erete
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics Library and Information Science Youth Experience, Learning, and Digital Practices
The CSGrad4US Fellowship Program supports domestic bachelor’s degree holders returning to academia by providing mentoring and funding to pursue PhDs in computing. Through this cooperative agreement, the Computing Research Association will manage recruitment, applications, mentoring, and program evaluation to expand diversity in CISE fields.
Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA)
Principal Investigator(s): Victoria Van Hyning
Funders: Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design Archival Science Digital Humanities Information Justice, Human Rights, and Technology Ethics Library and Information Science Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems.
Principal Investigator(s): Victoria Van Hyning
Funders: Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design Archival Science Digital Humanities Information Justice, Human Rights, and Technology Ethics Library and Information Science Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems.
DataGOAT – Building Counter Structures to Combat Systemic Racism in STEM Education & Sport Through Data Literacy
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Youth Experience, Learning, and Digital Practices
The DataGOAT project integrates data science education with college athletics to promote STEM engagement and racial equity, particularly for Black male athletes. By creating coursework, internships, and technical tools, the project empowers athletes with critical data literacy while challenging stereotypes and linking athletic data practices to meaningful academic and career pathways.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Youth Experience, Learning, and Digital Practices
The DataGOAT project integrates data science education with college athletics to promote STEM engagement and racial equity, particularly for Black male athletes. By creating coursework, internships, and technical tools, the project empowers athletes with critical data literacy while challenging stereotypes and linking athletic data practices to meaningful academic and career pathways.
Developing and Investigating Data Science Interventions Connected to University Athletics to Address Systemic Racism in Undergraduate STEM Education (better known as DataGOAT)
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
HCC: Small: The Incel Phenomenon: Assessing Radicalization and Deradicalization Online
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Health Informatics Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Health Informatics Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Information Technology Access RERC
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Federal
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
The University of Maryland Information Technology RERC aims to improve accessibility for people with disabilities through research, technology development, and standards creation. Key initiatives include individualizing generative AI, enhancing usability for older adults, developing cross-disability solutions, and creating open-source tools and guidelines to ensure broad, equitable access to information and communication technologies.
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Federal
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
The University of Maryland Information Technology RERC aims to improve accessibility for people with disabilities through research, technology development, and standards creation. Key initiatives include individualizing generative AI, enhancing usability for older adults, developing cross-disability solutions, and creating open-source tools and guidelines to ensure broad, equitable access to information and communication technologies.
Institute for Trustworthy AI in Law and Society (TRAILS)
Principal Investigator(s): Katie Shilton
Funders: 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
Funders: 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.