Big Data and Artificial Intelligence (AI) have become a major force that impacts our daily lives in essential ways, from how political messaging and marketing are designed, to automating the process of deciding who gets hired or which neighborhood should be intensely patrolled. Big Data and AI can be an important agent for social justice and equality; or they can also be used to perpetuate injustice and hurt populations that are already disadvantaged and marginalized. Artists have been at the forefront, together with scientists, in exploring ways in which AI systems can be more equitable, transparent and inclusive. This mini-symposium brings leading voices in the field together, and is inspired by two projects at U-M:
Stephanie Dinkins: On Love & Data, the first survey exhibition of this prominent transmedia artist whose work creates platforms for dialogue about AI as it intersects race, gender, aging and future histories. This exhibit is organized by Stamps Gallery, Penny W. Stamps School of Art & Design, from August 27 to October 23, 2021 and generously supported by the Andy Warhol Foundation for the Visual Arts.
Fair Representation in Arts and in Data, a collaboration between data scientists, artists and museum curators and funded by the U-M President’s Arts Initiative, the project team uses facial recognition technology to consider both the limitations of racial representation within UMMA’s collection and the limitations of the technology itself. The results culminate in an exhibit, “White Cube / Black Box”, which will open at U-M Museum of Art on October 16, 2021.
10:30am Opening Remarks
10:40-noon Keynote and Q&A
12:30-1:45 Art, Machine Learning and Data Justice Panel Discussion
2:30-4:00 Data Science and Machine Learning for Artists workshop
U-M Museum of Art, 525 S State St, Ann Arbor
This will be a non-technical exploration of methods and possibilities for the use of data science and machine learning in the arts. We will cover a few of the ways that creative works can be viewed as data, and consider how methods for learning from data can be used to advance creation and insight in the arts. Students pursuing degrees at all levels in any field of arts are especially encouraged to attend. No prior exposure to data science or machine learning is expected.
Director, Consulting for Statistics, Computing, and Analytics Research; Professor of Statistics, U-M
This mini symposium is co-organized by Stamps Gallery, Penny W. Stamps School of Art & Design, Digital Studies Institute, Michigan Institute of Data Science, University of Michigan Museum of Art, and ArtsEngine. Generously supported by the U-M Arts Initiative.