Preventing Harmful Data Practices by Using Participatory Input to Navigate the Machine Learning Multiverse

MCML Authors

Jan Simson

→ Group Christoph Kern
 Social Data Science and AI
→ Co-Group Frauke Kreuter

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Abstract

In light of inherent trade-offs regarding fairness, privacy, interpretability and performance, as well as normative questions, the machine learning (ML) pipeline needs to be made accessible for public input, critical reflection and engagement of diverse stakeholders. In this work, we introduce a participatory approach to gather<br>input from the general public on the design of an ML pipeline. We show how people’s input can be used to navigate and constrain the multiverse of decisions during both model development and evaluation. We highlight that central design decisions should be democratized rather than “optimized” to acknowledge their critical impact on the system’s output downstream. We describe the iterative development of our approach and its exemplary implementation on a citizen science platform. Our results demonstrate how public participation can inform critical design decisions along the model-building pipeline and combat widespread lazy data practices.

inproceedings

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CHI 2025

Conference on Human Factors in Computing Systems. Yokohama, Japan, Apr 26-May 01, 2025.

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Authors

J. Simson • F. Draxler • S. Mehr • C. Kern

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Links

DOI

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Research Area

 C4 | Computational Social Sciences