Advanced Search

Preventing harmful data practices by using participatory input to navigate the machine learning multiverse


Simson, Jan ; Draxler, Fiona ; Mehr, Samuel ; Kern, Christoph


[img] PDF
3706598.3713482.pdf - Published
Download (1MB)

DOI: https://doi.org/10.1145/3706598.3713482
URL: https://dl.acm.org/doi/10.1145/3706598.3713482
URN: urn:nbn:de:bsz:180-madoc-698022
Document Type: Conference or workshop publication
Year of publication: 2025
Book title: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Volume: Article 806
Page range: 1-30
Conference title: CHI 2025, Conference on Human Factors in Computing Systems
Location of the conference venue: Yokohama, Japan
Date of the conference: 26.04.-01.05.2025
Publisher: Yamashita, Naomi ; Evers, Vanessa ; Yatani, Koji ; Ding, Xianghua (Sharon) ; Lee, Bongshin ; Chetty, Marshini ; Toups-Dugas, Phoebe
Place of publication: New York, NY, USA
Publishing house: Association for Computing Machinery
ISBN: 979-8-4007-1394-1
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Keywords (English): participatory design , machine learning , algorithmic fairness , multiverse analysis, citizen science , garden of forking paths
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 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.




Dieser Eintrag ist Teil der Universitätsbibliographie.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




Metadata export


Citation


+ Search Authors in

BASE: Simson, Jan ; Draxler, Fiona ; Mehr, Samuel ; Kern, Christoph

Google Scholar: Simson, Jan ; Draxler, Fiona ; Mehr, Samuel ; Kern, Christoph

ORCID: Simson, Jan ORCID: 0000-0002-9406-7761 ; Draxler, Fiona ; Mehr, Samuel ; Kern, Christoph ORCID: 0000-0001-7363-4299

+ Download Statistics

Downloads per month over past year

View more statistics



You have found an error? Please let us know about your desired correction here: E-Mail


Actions (login required)

Show item Show item