Collaborative Design of Data and AI Systems for Science and Society

This project is part of MIDAS’s effort to develop effective public-private partnerships to advance research in the era of artificial intelligence (AI) and to maximize the benefits of such research to society. The advancement of AI brings forward unprecedented promises for breakthroughs in science and for vastly improved policy-making. But at the current moment, the fast pace of AI development actually poses major challenges to scientists in academia and to public-sector organizations. The advancement of AI technology far outpaces the capacity of individual researchers and individual organizations to adopt such technology; the development of such AI technology is also often not tightly coupled with considerations for their applications to scientific and government data. Using questionable AI technology or using it in questionable ways jeopardizes the validity and trustworthiness of scientific research and policy making. 

This project, funded by the National Science Foundation, will bring together data science and AI methodologists from the University of Michigan and Microsoft, University of Michigan scientists who apply such data science and AI methods across research fields, and the Detroit data team. Many AI systems are not yet optimal to deal with specialized data in scientific research and with government data. Conversely, much of the enormous amount of scientific data and government data are not constructed to leverage the new AI systems. Making data “AI ready” will be a continued priority as novel forms of AI continue to emerge that use diverse types of data representation and preparation. Coordinated database research and AI research will enable data and AI to be more compatible. This project aims to build a mechanism for academia, industry, and the public sector to collaborate and co-design research and development (R&D) directions to simultaneously improve data systems and AI systems. Such co-designed systems will be able to better address scientific and societal challenges and avoid the harm from the inappropriate use of data and AI, especially the harm on marginalized communities. 

Please send inquiries to the project PI, Dr. Jing Liu, MIDAS Executive Director, ljing@umich.edu.