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

要旨

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 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.

著者
Jan Simson
LMU Munich, Munich, Germany
Fiona Draxler
University of Mannheim, Mannheim, Germany
Samuel Mehr
University of Auckland, Auckland, New Zealand
Christoph Kern
LMU Munich, Munich, Germany
DOI

10.1145/3706598.3713482

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713482

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Participatory Design and Applications

G418+G419
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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