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STPP TAP

Showing 1 - 19 of 19 results
News

STPP research on AI highlighted in Nature Q&A

May 3, 2022
Ford School professor Shobita Parthasarathy was highlighted in a Q&A with Nature magazine, acknowledging recent research on Large Language Models (LLMs) by the Science, Technology, and Public Policy program's Technology Assessment Project....
In the Media

Parthasarathy facial recognition study in focus on Detroit Public TV

Jun 24, 2021 DPTV One Detroit
Detroit Public TV's One Detroit program looked at the racial disparities inherent in law enforcement's use of facial recognition technology, making reference to a study published in August 2020 by the Ford School's Shobita Parthasarathy.  "We have...
News

STPP wins grant to explore Large Language Models  

Jun 11, 2021
Large Language Models (LLM) — machine learning algorithms that can recognize, predict, and  generate human languages on the basis of very large text-based data sets — have captured the imagination of scientists, entrepreneurs, and tech-watchers....
State & Hill

Breaking down public trust

Jun 10, 2021
By Rebecca Cohen (MPP '09) Americans’ trust in government institutions to “do the right thing” has steadily eroded since the late 1960s,1 correlated for many analysts with events such as the Vietnam War, Watergate, the ’70s oil embargo, and...
In the Media

Parthasarathy provides insight on vaccine patterns

May 18, 2021 The Hill
As vaccine rates increase across the country, interesting patterns are being noticed. Shobita Parthasarathy, professor of public policy, explained the pattern Michigan is experiencing.  “Michigan is sort of a purple, leaning blue, state and you...
In the Media

Parthasarathy puts results of vaccine hesitancy study in context

May 12, 2021 WXYZ Detroit
A recent study from researchers at U-M concluded that vaccine hesitancy could impede a goal of herd immunity when it comes to COVID-19. Shobita Parthasarathy, a co-author of the study and director of the Ford School's Science, Technology, and Public...
Technology Assessment Project

Facial Recognition in Schools

September 2019 - August 2020
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Claire Galligan, Hannah Rosenfeld, Molly Kleinman, Shobita Parthasarathy
Facial recognition (FR) technology was long considered science fiction, but it is now part of everyday life for people all over the world. FR systems identify or verify an individual’s identity based on a digitized image alone, and are commonly used for identity verification, security, and surveillance in a variety of settings including law enforcement, commerce, and transportation. Schools have also begun to use it to track students and visitors for a range of uses, from automating attendance to school security. FR can be used to identify people in photos, videos, and in real time, and is...
Technology Assessment Project

Vaccine Hesitancy

September 2020 - May 2021
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Zixuan Wang, Margarita Maria Rodriguez Morales, Kseniya Husak, Molly Kleinman, Shobita Parthasarathy
In winter 2020, a novel coronavirus (SARSCoV-2) that caused COVID-19 started its spread across the globe, and by July 2020, over 500,000 people worldwide had died of the disease. By March 2021, there were over 120 million cases and over 2.8 million deaths. To combat the pandemic and return to “normalcy”, experts estimate that at least 80% of the world’s population needs to be resistant to the virus, and most of the world’s population will require vaccination. This will be a challenge. In addition to facilitating widespread distribution, governments will need to combat “vaccine hesitancy”: an...
Technology Assessment Project

What’s in the Chatterbox?

May 2021
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Johanna Okerlund, Evan Klasky, Aditya Middha, Sujin Kim, Hannah Rosenfeld, Molly Kleinman, Shobita Parthasarathy
Large language models (LLMs)—machine learning algorithms that can recognize, summarize, translate, predict, and generate human languages on the basis of very large text-based datasets—are likely to provide the most convincing computer-generated imitation of human language yet. Because language generated by LLMs will be more sophisticated and human-like than their predecessors, and because they perform better on tasks for which they have not been explicitly trained, we expect that they will be widely used. Policymakers might use them to assess public sentiment about pending legislation,...