Amelia Acker
Institute of Museum and Library Services (IMLS)
06/01/2018 to 01/31/2024
The collaborative award is $199,811 over the project period. The School of Information portion of the award is $38,932.
Investigating Platform Development for Mobile and Social Media Data Preservation
The information we generate on social media sites and in mobile device apps represents the fastest form of data creation and collection in the United States. However, these data traces are complicated to work with because they are varied, inter-dependent, and vulnerable to loss. In this Early Career Development project, Dr. Amelia Acker at the University of Texas at Austin, will conduct a three-year, qualitative investigation into the activities of engineers and designers at five institutions where social media software is being developed. This project to better understand developer cultures will aid archives, libraries, and museums as they develop and implement best practices for gathering and preserving social media collections.
Matthew Lease
Daniel Stanzione, William Barth, Niall Gaffney, Tommy Minyard, and Paul Navratil
National Science Foundation (NSF)
06/01/2016 to 03/31/2024
The collaborative award is $30,000,000 over the project period. The School of Information portion of the award is $172,281.
Stampede 2: The Next Generation of Petascale Computing for Science and Engineering
The Texas Advanced Computing Center (TACC) at the University of Texas at Austin will acquire and deploy Stampede 2, a new, nearly 20 petaflop High Performance Computing (HPC) system. This system will be available to and accessed by thousands of researchers across the country. It will enable new computational and data-driven scientific and engineering, research and educational discoveries and advances. As a national resource, Stampede 2 will replace and surpass the current highly successful Stampede system. The new system will deliver over twice the overall performance as the current system in many dimensions most important to scientific computing, including computing capability, storage capacity, and network bandwidth. TACC and its academic partners will team with Dell, Inc. and Intel Corp. to procure and provide this system.
HPC is intrinsic to discovery across the science and engineering disciplines served by the NSF. This resource allows researchers to explore those scientific and engineer frontiers that require very large scale computations not otherwise possible. Over the life of Stampede 2, the system is expected to serve many thousands of researchers spanning all NSF-supported disciplines, as the current system has done. In addition to being an immediately productive resource for a large community of computational engineers and scientists, Stampede 2 will also continue the community on an evolutionary path to future "many core" computing technologies.
Stampede 2 will employ upcoming generations of Intel's Xeon and Xeon Phi processors, as well as the Intel Omni-Path network fabric. The system will maintain a familiar Linux-based software environment to insure a smooth migration of the large existing user base to the new system. The system and its software stack will be designed to support traditional large scale simulation users, users performing data intensive computations, as well as emerging classes of new and non-traditional users to high performance computing. Stampede 2 will support breakthrough discoveries and advances across a wide range of research topics.
Matthew Lease
Micron Technology Inc.
08/01/2019 to 07/31/2022
The award is $150,000 over the project period.
Tackling Misinformation through Socially-Responsible AI
While the broad goals of socially responsible artificial intelligence (AI) appear clear in the abstract, how can we translate such goals into practice for a real problem facing our society today? We consider the following challenge: How can we design responsible AI technologies to curb the digital spread of misinformation?
Exploring real use cases and interface designs, we develop prototype AI applications and user-centered evaluations to remedy situations in which misinformation circulates online.
Min Kyung Lee
Zhiwei Steven Wu (University of Minnesota-Twin Cities)
National Science Foundation (NSF)
01/01/2020 to 12/31/2023
The collaborative award is $581,013 over the project period. The School of Information portion of the award is $218,981.
FAI: Advancing Fairness in AI with Human-Algorithm Collaborations
Artificial intelligence (AI) systems are increasingly used to assist humans in making high-stakes decisions, such as online information curation, resume screening, mortgage lending, police surveillance, public resource allocation, and pretrial detention. While the hope is that the use of algorithms will improve societal outcomes and economic efficiency, concerns have been raised that algorithmic systems might inherit human biases from historical data, perpetuate discrimination against already vulnerable populations, and generally fail to embody a given community's important values. Recent work on algorithmic fairness has characterized the manner in which unfairness can arise at different steps along the development pipeline, produced dozens of quantitative notions of fairness, and provided methods for enforcing these notions. However, there is a significant gap between the over-simplified algorithmic objectives and the complications of real-world decision-making contexts. This project aims to close the gap by explicitly accounting for the context-specific fairness principles of actual stakeholders, their acceptable fairness-utility trade-offs, and the cognitive strengths and limitations of human decision-makers throughout the development and deployment of the algorithmic system.
To meet these goals, this project enables close human-algorithm collaborations that combine innovative machine learning methods with approaches from human-computer interaction (HCI) for eliciting feedback and preferences from human experts and stakeholders. There are three main research activities that naturally correspond to three stages of a human-in-the-loop AI system. First, the project will develop novel fairness elicitation mechanisms that will allow stakeholders to effectively express their perceptions on fairness. To go beyond the traditional approach of statistical group fairness, the investigators will formulate new fairness measures for individual fairness based on elicited feedback. Secondly, the project will develop algorithms and mechanisms to manage the trade-offs between the new fairness measures developed in the first step, and multiple existing fairness and accuracy measures. Finally, the project will develop algorithms to detect and mitigate human operators' biases, and methods that rely on human feedback to correct and de-bias existing models during the deployment of the AI system.
Amelia Acker
Megan Finn, University of Washington;
Ryan N. Ellis, Northeastern University
National Science Foundation (NSF)
02/01/2021 to 01/31/2024
The collaborative award is $199,811 over the project period. The School of Information portion of the award is $38,932.
RAPID International Type I: Collaborative Research: COVID Data Infrastructure Builders: Creating Resilient and Sustainable Research Collaborations
The COVID-19 pandemic has sparked thousands of new large-scale data projects globally. These COVID data infrastructures are essential: they enable the public, policymakers, public health officials, and others to see and comprehend particular aspects of the global health crisis. This research compares COVID data infrastructures in the U.S. and India, countries that share extremely high COVID infection rates as well as electoral democracy that encourages transparency; 'Data for Social Good' rhetoric; and large IT workforces. The project seeks to reveal how project leaders and contributors confront and manage the disruptions, hardships, and conflicts created by the pandemic. Working across different geographies and institutional settings, the research project will highlight how the pandemic impacts different communities in different ways. The research project will provide policymakers, technologists, and other leaders with insights and recommendations on how to improve the creation and maintenance of emergency data infrastructures. By understanding the dynamics of current COVID data infrastructures, we can be better prepared for the next emergency.
This RAPID research project investigates the creation, maintenance, and real-time transformation of novel critical data infrastructures. It uncovers the debates, conflicts, orderings, and important decisions that shape and define COVID data-tracker systems. At a time when the pandemic is disrupting ongoing research across the globe, these data-trackers can provide insights into how to create and maintain resilient and sustainable research-enabling infrastructure under conditions of significant stress. This RAPID project uses cross-national comparative analysis of public COVID data projects in the U.S. and India in order to identify the key factors that enable data infrastructures to endure the social and material disruptions associated with the pandemic. The project's cross-national and comparative research design ensures that research findings are generalizable. COVID data infrastructures are dynamic: the information, practices, tools, and collaborators that populate these systems constantly evolve. Often, the important adaptations that shape critical data infrastructures are not easily preserved using current web archiving and cumulative public data preservation methods. Additionally, the project's research design will capture this otherwise ephemeral data--allowing the project to analyze and interpret how these infrastructures are created and maintained under adverse conditions. The project is informed by and will contribute to the scholarly literature on ethnographies of technology development, infrastructure studies, and crisis informatics. Research findings will support concrete recommendations for how these and future data infrastructure can be made (1) sustainable; (2) accountable to different publics; and (3) improved in order to help save lives.