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ACROCPoLis: a descriptive framework for making sense of fairness
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Responsible Artificial Intelligence)ORCID iD: 0000-0002-8423-8029
Umeå University, Faculty of Arts, Department of historical, philosophical and religious studies.ORCID iD: 0000-0002-0464-3535
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0002-1112-2981
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
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2023 (English)In: FAccT '23: Proceedings of the 2023 ACM conference on fairness, accountability, and transparency, ACM Digital Library, 2023, p. 1014-1025Conference paper, Published paper (Refereed)
Abstract [en]

Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and are experienced by individuals and social groups. In this paper, we do this by means of proposing the ACROCPoLis framework to represent allocation processes with a modeling emphasis on fairness aspects. The framework provides a shared vocabulary in which the factors relevant to fairness assessments for different situations and procedures are made explicit, as well as their interrelationships. This enables us to compare analogous situations, to highlight the differences in dissimilar situations, and to capture differing interpretations of the same situation by different stakeholders.

Place, publisher, year, edition, pages
ACM Digital Library, 2023. p. 1014-1025
Keywords [en]
Algorithmic fairness; socio-technical processes; social impact of AI; responsible AI
National Category
Information Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-209705DOI: 10.1145/3593013.3594059Scopus ID: 2-s2.0-85163594710ISBN: 978-1-4503-7252-7 (print)OAI: oai:DiVA.org:umu-209705DiVA, id: diva2:1766810
Conference
2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, Illinois, USA, June 12-15, 2023
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-07-18Bibliographically approved

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Aler Tubella, AndreaCoelho Mollo, DimitriDahlgren, AdamDevinney, HannahDignum, VirginiaJonsson, AnnaKampik, TimotheusMendez, Julian AlfredoNieves, Juan Carlos

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Aler Tubella, AndreaCoelho Mollo, DimitriDahlgren, AdamDevinney, HannahDignum, VirginiaEricson, PetterJonsson, AnnaKampik, TimotheusLenaerts, TomMendez, Julian AlfredoNieves, Juan Carlos
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Department of Computing ScienceDepartment of historical, philosophical and religious studies
Information Systems

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CiteExportLink to record
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Citation style
  • apa
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