The Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA) teamed up this past week to host the University of Michigan Annual Data Science and AI Summit, held in conjunction with the Academic Data Science Alliance Annual Meeting Oct. 29-31 at the Michigan Union.
The gathering attracted 275-plus attendees from over 100 universities and industry, government, and community organizations, with keynote addresses from Janet Haven, the executive director of Data & Society, and Maggie Levenstein, the Director of the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. Keynotes were also given by Zahra Khanjani of the University of Maryland Baltimore County as well as Montgomery College’s Alexandra Veremeychik.
“Our ADSA Annual Meeting is an opportunity for the data science community to come together and share their advances and best practices,” said Micaela Parker, founder and executive director of ADSA. “Our ADSA community has an open-arms approach to data science and AI – we welcome all disciplines, from philosophy and the arts to computer science and robotics. This transdisciplinary vision is shared by MIDAS and allowed us to involve researchers students, and community members from across the country who shared their work and perspectives for the future of data science and AI.”
“Hosting the conference complements MIDAS efforts towards responsible research, training and collaboration,” remarked H.V. Jagadish, director of MIDAS. “Welcoming hundreds of attendees from esteemed colleges and universities around the world, as well as a continued partnership with ADSA, allows us to advance data science and AI research at U-M and in the broader community.”
The central theme of this year’s Annual Meeting was “Data Science and AI – Keeping Humans in the Loop.” The summit focused on the crucial role that humans play in data and AI, while also addressing the need to prepare the future workforce for ongoing discovery and innovation.
Session topics included data science education, AI, data science for social impact, and data science research and applications. Various workshops explored data access through ICPSR, data science training for social impact education, project-based learning in data science, utilizing data for good in education, analytical storytelling, teaching ethics in data science, and unlocking census data.