Scores for Data Professionals
Mitigating Algorithmic Harm
Are you a data professional concerned about algorithmic harm?
Are you interested in exploring different ways to mitigate potential algorithmic harm together with other data professionals?
If yes, please submit an expression of interest form (button below), and Angela (project facilitator) will reach out to you as soon as possible about next steps. For more information about the project, see FAQs below.
Q: Who is a data professional?
A: Someone involved in algorithm design, implementation, and/or application.
Q: What is algorithmic harm?
A: 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, reinforcing socioeconomic inequality. This project considers these events algorithmic harms.
Q: What do data professionals have to do with algorithmic harm?
A: There are a lot of organizations and individuals involved in producing situations that might lead to algorithmic harm. Data professionals are one of many of these groups of individuals that bear some degree of responsibility through their decision-making around data use, sharing, and storage.
Importantly, data professionals' ability to work towards mitigating algorithmic harm in their decision-making can be limited by a number of factors – conditions of employment (e.g., power dynamics with a boss, job description, degree of control over various algorithmic implementation processes, etc.), institutional norms, societal norms, etc.
This project aims to identify the opportunities and constraints of our agency as data professionals, to make decisions that can mitigate algorithmic harm.
Q: What do you mean by work together with other data professionals:
A: Participating in this project involves meeting virtually 6 times over 6 months (February-July 2024) for a focus group with 3-6 other participants. Each meeting will last about 1.5 hours.
Q: What will focus group meetings entail?
A: Each focus group will include some balance of discussion, improvisation, and score-making. Improvisation and scores are two methods from movement arts. Improvisation will help us identify where in our work our perspectives as people might be affecting our decision-making without our conscious awareness in ways that might perpetuate algorithmic harms down the road. We will use scores to structure our collaboration.
Q: Do I need to know what improvisation and scores are to participate?
A: No. Prior knowledge or experience with these methods is not required. You do, however, need to be open to and interested in physically moving and eventually facilitating movement experiences for fellow participants.
Q: What are the eligibility requirements to participate in this project?
A: To participate in this project, you must:
Be currently engaged in some aspect of algorithm design, implementation, or application.
Be interested in and open to physically moving and facilitating movement experiences for fellow participants. Prior experience doing these things is not necessary, but a keen interest and openness in doing so is.
Be available to meet virtually with the group 6 times between February and July 2024 (1.5 hours/meet-up).
Age 18 or above.
Consent to participate. The facilitator will walk you through all the details of what consenting to participate means once you've submitted an expression of interest form.
Q: What can I expect to get out of participating in this project?
A: There are a few things you might expect to get out of participating in this project:
Developing or deepening skills in facilitation, collaboration, and self-reflection,
Developing or deepening relationships with other data professionals,
Increased awareness of what steps may be feasible for you to take in your own context as a data professional to mitigate algorithmic harm.
Q: Who is facilitating this project?
A: 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.