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Scores* for Data Professionals

Mitigating Algorithmic Harm

Together with other data professionals, in this project we explore how improvisation and scores (two well-established methods in movement arts) can help us answer the research questions:


What steps can data professionals take to mitigate algorithmic harm? 

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Want to participate in this project? Fill out the participation interest form by clicking the "Participate" button below!

* By score, we mean a method in the tradition of movement arts.


In the last decade, we've learned that algorithms can perpetuate social harms. For example, in 2020 a false positive match by a facial recognition algorithm led to the first suspected wrongful detention of a person for a crime they didn't commit. In another example, web behavior tracking algorithms targeted lower-income people with predatory ads for for-profit universities, leading thousands to fall into debt thus reinforcing socioeconomic inequality. This project considers these events algorithmic harms.


Read more about what this project means by algorithmic harm by clicking on the button below.


A range of institutions and scholars have named principles, priorities, and ethics that algorithm design, implementation, and application can follow to mitigate algorithmic harm. 

This project asks how a particular group people involved in algorithmic design, implementation, and application – data professionals – can mitigate algorithmic harm by practically realize these principles in their day-to-day operations.

Read more about what this project looks like by clicking on the button below.


This project uses two well-established methods in movement arts – improvisation and scores – in a virtual focus-group style format.


No prior experience with these methods is required to participate in this project, but openness to moving and digging into embodied knowledge is!

Read more about these methods and why we use them  by clicking on the button below.


This project is a part of Angela Schöpke Gonzalez's PhD dissertation at the University of Michigan School of Information. The project is funded by the University of Michigan School of Information. Angela organizes and facilitates this project.


Read more about Angela by visiting her website via the button below. Angela's website will open in a new tab.

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