Technology Assessment Project

A research-intensive think tank

STPP's Technology Assessment Project (TAP) research anticipates the implications of emerging technologies and uses these insights to develop better technology policies.

At its root, vaccine hesitancy is a problem of public mistrust in institutions.
Shobita Parthasarathy in WXYZ Detroit

We use an analogical case study approach to analyze the social, economic, ethical, equity, and political dimensions of emerging technologies, such as facial recognition, autonomous vehicles, CRISPR therapy in humans, and COVID contact tracing apps. Our distinctive evaluation approach can be applied to technologies in a range of areas.

If you have ideas/suggestions for emerging technologies that you would like TAP to tackle, please email us at stpp@umich.edu.

LATEST RESEARCH: With generous support from the Alfred P. Sloan Foundation, last fall STPP researched the social, ethical, and equity implications of large language models—machine learning algorithms that can recognize, predict, and generate human languages on the basis of very large text-based data sets. Read more below.

In this report, TAP researchers analyze the social, ethical, and equity implications of large language models—machine learning algorithms that can recognize, predict, and generate human languages on the basis of very large text-based data sets.

TAP researchers break down reasons for vaccine hesitancy, which could stand in the way for COVID-19 herd immunity. The study finds two main causes of public mistrust: limitations and failures in scientific and technical institutions, and institutionalized mistreatment of marginalized communities.

Although facial recognition technology represents a global $3.2 billion business, TAP researchers say its use should be banned in schools. The report finds the technology not suited to security purposes, and it creates a web of serious problems, including racial discrimination, normalizing surveillance and eroding privacy, institutionalizing inaccuracy and creating false data on school life, commodifying data, and marginalizing nonconforming students.

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