Participatory AI in Marginalized Communities

Exploring Strategies for Inclusive Stakeholder Engagement in Algorithmic Development

Master thesis (2023)

Authors

C. El Moussaoui Electrical Engineering, Mathematics and Computer Science

Contributors

C.C.S. Liem (supervisor 1)
L. Cavalcante Siebert Interactive Intelligence - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Chakir El Moussaoui
Public Sector Algorithmic decision-making Participatory AI Marginalized Communities
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Published Date

20-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In today's society, the rapid progression of digitization has led to the automation of various facets of human existence. This transformation has been facilitated by the utilization of algorithms, which are instrumental in driving efficient and effective automated processes. These algorithms have also found widespread adoption in the public sector, where they are employed to streamline and optimize various tasks and operations. The integration of algorithms in the public sector has brought about significant advancements in areas such as predictive policing, social welfare allocation, and healthcare.

However, the use and development of these automated processes were subjected to concerns from the public about privacy, bias, accountability, and transparency. Since these concerns are mainly coming from citizens, their involvement in the process of developing algorithmic systems can potentially be of help.

We explore the potential of participatory AI in marginalized communities as a means of obtaining valuable input from citizens regarding the development of these algorithmic systems employed by the public sector. One Piece of our approach involves hosting discussions in local community centers in marginalized neighborhoods. Our focus is on dilemmas that are relevant to algorithm design and evaluation decisions, and we frame these dilemmas in various ways, including forms that may not directly relate to societal impact, but are understandable for laypeople. Our key findings suggest that involving marginalized citizens can bring valuable perspectives and insights that are otherwise ignored. By incorporating public perspectives into algorithm development, we can promote inclusive decision-making processes and ensure that algorithms align with community values.