Read more: myumi.ch/O4znq
Depictions of people in museum visual arts collections often reflect the histories of inclusion and exclusion. For example, museum collections in the 1920s likely reflect much less the lives of African Americans than those in the 1960s. U-M Museum of Art’s (UMMA) entire collection of ~24,000 pieces of artwork, spanning 150 years, has been digitized. A group of data scientists and artists will apply algorithms to recognize faces and to examine how art collections can (mis)represent humanity and how that representation changes with time.
The project team includes:
- Jing Liu, Michigan Institute for Data Science (MIDAS)
- Kerby Shedden, MIDAS, Consulting for Statistics, Computing and Analytics Research and Dept. Statistics
- David Choberka and John Turner (UMMA)
- Sophia Brueckner (Stamps School of Art & Design).
This project, funded by the U-M Arts Initiative, is the result of months of discussion about how data science and arts can give each other a stronger voice to promote social justice.
The issue of fair and unbiased representation looms large as Big Data and Artificial Intelligence (AI) are impacting many aspects of our society, from political messaging to targeted marketing, from parole granting to hiring practices. With biased data, algorithms will reach superficially compelling but ultimately false conclusions and amplify the biases and the injustices that exist in the data. This project will illustrate this point by generating a collection of composite representations of human faces from the UMMA dataset, and presenting these representations through an art installation, interactive digital displays and narratives. Through this new way to communicate research that is understandable, meaningful, and impactful to the public, this project highlights the caveats that lie beneath Big Data and AI applications and inspire the public to participate in shaping our transition into a data-driven society.
Drs. Josh Pasek (LSA Communication and Media), Michael Traugott (LSA Political Science), Ceren Budak (School of Information) and Stuart Soroka (LSA Communication and Media), along with their Georgetown colleagues, have been collaborating with CNN to improve survey questions and carry out data analysis. This work is partly an extension of the Challenge Award project that MIDAS has funded. Read more: https://www.cnn.com/2020/08/16/politics/election-2020-polls-the-breakthrough-methodology/index.html
In collaboration with the US Environmental Protection Agency (EPA) National Vehicle and Fuel Emissions Laboratory (NVFEL), MIDAS will develop and administer a comprehensive educational program during the 2020-2021 academic year. The overall goal of the program is to support the EPA in its mission of improving air quality through applied data science, while providing experiential learning opportunities for students. In this program, senior-level undergraduate and graduate students will apply advanced data science techniques to real-world problems related to reducing the environmental impact of personal and freight mobility systems, design systems to reduce overall vehicle emissions using connected and autonomous vehicles, and engage in environmental policy research by analyzing the potential environmental benefits shared and/or automated vehicles.
EECS 498-009 will be on Reducing Emissions through Applied Data Science, the course syllabus is attached.
The app created by Danny Forger and team allows users to understand how their own body clocks have been impacted by social distancing and provides researchers with anonymized data to study the impact of disrupted circadian rhythms on a person’s health.
“Social Distancing and the lockdowns have affected our sleep and circadian (daily) rhythms. To address this, we have developed a “social rhythms” iPhone app that sends you a report on how your circadian timekeeping has changed since the COVID epidemic began, as well as general information about your circadian rhythms (e.g., when to seek light), based on phone, wearable data (iPhone, Fitbit, Mi Band…) and mathematical models and algorithms developed at the University of Michigan.
We hope this app can be helpful both while social distancing, and as we prepare for our new normal. The app works best for individuals who carry their phones around with them or use wearables. While your data is on our servers, we will use it for research, and hopefully, you will receive future reports through the app. You can also remove your data in the settings area of the app. All data and reports are sent anonymously. The more you and others use the app, the more we learn.”