The University of Arizona prioritizes technological development, 4th IR ventures, and data science that is socially responsible and aligned with public interests. Indication of this commitment includes the recently-funded PIT-UN project entitled: Data and Community Science: Getting minoritized undergraduates into the field of Data Science for Public Interest. We also emphasize foci on social impacts as exemplified by the recent NSF-funded Engineering Research Center for Quantum Networks. While developing the foundation for the future of quantum networking, that is, researchers are planning for changes in Internet governance and researching human-usability matters alongside the technical engineers. More information about social impacts research relative to the emerging quantum network can be found here: https://cqn-erc.org/research/thrust-4/. We see this example as a model for future University of Arizona research and training efforts that emphasize human factors alongside technical foci.
Catherine Brooks, iSchool Director & Associate Professor and Founder, Center for Digital Society and Data Studies
Data and Community Science: Getting minoritized undergraduates into the field of Data Science for Public Interest
A big obstacle for drawing minoritized students into data science is unawareness of what various data science careers entail. We aim at offering data science mentoring and training to undergraduates interested in public good, connecting them with summer internships at local community organizations. The project is expected to increase awareness of what data science is, how it can improve communities, and what data science skills careers are available now and emerging in the future. We will provide professional skills training to minoritized (in terms of race, ethnicity, gender, and disability) undergraduate students pursuing a career in community-focused data science. Our outreach program will connect local community organizations with minoritized undergraduate students for data science internships and shed light on how community-university collaborations can increase diversity in the field of data science.