As data science and Artificial Intelligence (AI) are shaping our society in unprecedented yet unpredictable ways, organizations in the public and private sectors are all developing data and AI strategies. The effective use of data and AI in decision-making can greatly empower communities; but as with any new technology, data and AI also pose dangers to create insight gaps and to amplify existing injustice.
Like many academic data science organizations, the Michigan Institute for Data and AI in Society (MIDAS) has been creating opportunities for researchers to support public sector organizations’ data strategies. A recent such Data for Social Good project is a collaboration with the Little Traverse Bay Bands of Odawa Indians (LTBB), which started in Summer 2021 by a team of MIDAS faculty and students. The team focused on modernizing methods for storage of student data and addressing the need to accurately track and analyze academic performance, career choices, and other learning outcomes. This project was LTBB’s first step towards building a comprehensive data strategy to guide the work of the tribal government to understand their citizens’ needs and better seek and allocate resources.
Encouraged by the initial success of the project, LTBB and MIDAS are taking their effort to the next level. They have secured a National Science Foundation (NSF) Civic Innovation Challenge stage 1 grant to plan a centralized and comprehensive database that helps to accurately capture the condition of the Tribal Nation and its citizens. The team is led by H. V. Jagadish, director of MIDAS; with co-PIs from both U-M (Jing Liu, executive director of MIDAS; Tayo Fabusuyi, assistant research scientist at the U-M Transportation Research Institute and MIDAS affiliated faculty member) and LTBB (Jordan Shananaquet, Director of the Niigaandiwin Education Department). They will employ multiple components of cutting-edge database research to build the database, which will become a robust tool for assessing citizen needs and implementing data-driven policy aligned with LTBB constitutional directives while also prioritizing privacy, security, and accessibility.
It is often challenging to connect cutting-edge research with the immediate needs of the community. This NSF grant is our experiment to do exactly that. Looking forward, this project could also serve as a framework for other Tribal Nations who are looking to adopt a state-of-the-art approach to achieve data sovereignty. MIDAS is honored to be able to support this effort.
H. V. Jagadish