Ying Ding
Yan Leng, and Samuel Craig Watkins, University of Texas at Austin;
Yifan Peng Weill Cornell Medicine
AIM-AHEAD and National Institutes of Health (NIH)
09/17/2023 to 09/16/2025
The collaborative award is $998,739 over the project period. The School of Information portion of the award is $698,739.
Closing the loop with an automatic referral population and summarization system
Suicide is a public health concern and is ranked as the second leading cause of death in 10-24 years old.1,2 In particular, the increasing rates of suicide mortality and suicidal ideations and behaviors among Black youth in the United States (US) have become a pressing concern in recent years.3 Between 2001 and 2015, Black children under 13 years old were twice as likely to die by suicide, compared to their White counterparts.4 Furthermore, suicide mortality rates among Black youth have risen more rapidly than in any other racial or ethnic group.2,5 However, there remains a significant knowledge gap in understanding culturally tailored suicide prevention strategies for this population, particularly regarding unique social risk factors specific to Black youth. Specifically, a detailed understanding of social risk factors unique to Black youth and their differentiation from risk factors for other racial and ethnic groups is limited.2 This knowledge gap is critical, as research has indicated that Black youth face greater exposure to adverse childhood experiences (ACEs), which are linked to higher risks of suicidal ideation and attempts.
The National Violent Death Reporting System (NVDRS) is a state-based violent death reporting system in the U.S. that helps provide information and context on when, where, and how violent deaths occur and who is affected.11 However, much of the information in NVDRS is unstructured, limiting its use in examining a complete picture of the social risk factors contributing to Black youth suicide. Therefore, it is imperative to develop machine learning (e.g., natural language processing [NLP]) algorithms to automatically extract social risk factors from free text to help analyze Black youth suicide. Our long-term goal is to reduce the suicide rate by developing novel interventions targeting risk and protective factors among Black youth. The overall objective of this application is to develop and validate new AI approaches to identify individual-level social risks of Black youth suicide and enhance trust within the underserved communities regarding the approaches of AI/ML.
Ahmer Arif
National Science Foundation (NSF)
10/01/2022 to 09/30/2024
The collaborative award is $5,000,000 over the project period. The School of Information portion of the award is $1,368,142
NSF Convergence Accelerator Track F: Co-designing for Trust: Reimagining Online Information Literacies with Underserved Communities
In 2011, the National Science Foundation began requiring that all funded projects provide data management
plans (DMPs) to ensure that project data, computer codes, and methodological procedures were available to other
scientists for future use. However, the extent to which these data management requirements have resulted in more and
better use of project data remains an open question. This project thus investigates the National Science Foundation's
DMP mandate as a national science policy and examines the broad impacts of this policy across a strategic sample of five
disciplines funded by the National Science Foundation. It considers the organization and structure of DMPs across fields,
the institutions involved in data sharing, data preservation practices, the extent to which DMPs enable others to use
secondary project data, and the kinds of data governance and preservation practices that ensure that data are sustained
and accessible. Systematic investigation of the impact of DMPs and data sharing cultures across fields will assist funding
agencies and research scientists working to produce reproducible and open science by identifying barriers to data
archiving, sharing, and access. The principal investigators will use project findings to develop data governance guidelines
for information professionals working with scientific data and to articulate best practices for scientific communities
using DMPs for data management.
This project aims to enhance understanding of the role data management plans (DMPs) play in shaping data life-cycles.
It does so by examining DMPs across five fields funded by the National Science Foundation to understand data practices,
archiving and access issues, the infrastructures that support data sharing and reuse, and the extent to which project
data are later used by other researchers. In phase I, the investigators will gather a strategic sample of DMPs
representing a wide range of data types and data retention practices from different scientific fields. Phase II consists of
forensic data analysis of a subset of DMPs to discover what has become of project data. Phase III develops detailed case
studies of research project data life-cycles and data afterlives with qualitative interviews and archival documentary
analysis to help develop best practices for sustainable data preservation, access, and sharing. Phase IV will translate
findings into data governance recommendations for stakeholders. The project thus contributes to research about
contemporary studies of scientific data production and circulation while assessing the effect of DMPs as a national
science policy initiative affecting data management practices in different scientific communities. The comparative
research design and mixed methods enables theory building about cross-disciplinary data practices and data cultures
across fields and advances knowledge within data studies, information management studies, and science and
technology studies.
Soo Young Rieh
Kenneth Fleischmann and R. David Lankes
Institute of Museum & Library Services (IMLS)
08/01/2022 to 07/31/2025
The award is $623,501 over the project period.
Training Future Faculty in Library, AI, and Data Driven Education and Research (LADDER)
The University of Texas at Austin School of Information will collaborate with librarians from Austin Public Library, Navarro High School Library, and the University of Texas Libraries to educate and mentor the next generation of Library and Information Science (LIS) faculty with expertise in artificial intelligence (AI) and data science. The Training Future Faculty in Library, AI, and Data Driven Education and Research (LADDER) program will apply a new Library Rotation Model to train doctoral student fellows to apply their AI and data science skills to conduct research in collaboration with librarians in distinct library settings. The project will increase the capacity of LIS programs to educate the librarians of tomorrow by preparing cohorts of outstanding future faculty who understand both cutting-edge IT and the unique service environment of libraries.
Min Kyung Lee
Haiyi Zhu (Carnegie-Mellon University)
National Science Foundation (NSF)
10/01/2020 to 09/30/2024
The collaborative award is $2,013,764 over the project period. The School of Information portion of the award is $266,000.
SCC-IRG Track 1: Empowering and Enhancing Workers Through Building A Community-Centered Gig Economy
The gig economy is characterized by short-term contract work performed by independent workers who are paid in return for the "gigs" they perform. Example gig platforms include Uber, Lyft, Postmates, Instacart, UpWork, and TaskRabbit. Gig economy platforms bring about more job opportunities, lower barriers to entry, and improve worker flexibility. However, growing evidence suggests that worker wellbeing and systematic biases on the gig economy platforms have become significant societal problems. For example most gig workers lack financial stability, have low earning efficiency and lack autonomy, and many have health issues due to long work hours and limited flexibility. Additionally, gig economy platforms perpetuate biases against already vulnerable populations in society. To address these problems, this project aims to build a community-centered, meta-platform to provide decision support and data sharing for gig workers and policymakers, in order to develop a more vibrant, healthy, and equitable gig economy.
The project involves three major research activities. (1) Working with gig workers and local policymakers to understand their concerns, challenges, and considerations related to gig worker wellbeing, as well as the current practices, problems, and biases of existing gig economy platforms. (2) Developing a data-driven and human-centered decision-assistance environment to help gig workers make "smart" decisions in navigating and selecting gigs,and provide a macrolevel perspective for policymakers working to balance their diverse set of objectives and constraints. (3) Deploying and evaluating whether and how the above environment addresses the fundamental problems of worker wellbeing and systematic biases in the gig economy.
Min Kyung Lee
Carin Håkansta (Karolinska Institutet)
Karolinska Institutet
07/01/2023 to 12/31/2025
The School of Information allocation from this collaborative award is $28,381.
ALGOSH: Algorithmic management at work - challenges, opportunities, and strategies for occupational safety and health and wellbeing
Algorithms are at the forefront of a transformative shift in the World of Work, profoundly influencing work dynamics, organizational structures, and the work environment. Despite their profound impact, a substantial knowledge gap exists concerning algorithmic management (AM) and its repercussions on occupational safety, health, and wellbeing. This gap is particularly pronounced in non-platform work settings, where AM's prevalence is growing.
As the use of AM continues to expand across various economic sectors, it is imperative to investigate its effects on the wellbeing of workers. The overarching objective of the ALGOSH research program is to enhance our understanding of AM in non-platform sectors and its impact on the health, safety, and wellbeing of workers. Moreover, it aims to develop tools and strategies to mitigate associated risks. The three research aims of ALGOSH are:
- Facilitating the development of a standard for measurement of algorithmic management at work and related risks for health, safety and well-being.
- Increasing knowledge about the effects of algorithmic management on workers’ health, safety, and well-being.
- Investigating the balance of interests related to the control of algorithms in different legal contexts regarding occupational health and safety (OSH).
To accomplish this mission, an international and interdisciplinary consortium of researchers has been assembled. For our research to have maximum societal impact, the program also has a strong stakeholder involvement and support from trade unions, business organizations, international bodies, and government agencies. Their collective efforts will examine, discuss, and assess the opportunities and challenges posed by algorithmic management, fostering a safer and healthier work environment for all. The program applies multiple methods including quantitative, qualitative, literature reviews and participatory research.