ACROCPoLis: A Descriptive Framework for Making Sense of Fairness

Andrea Aler Tubella; Dimitri Coelho Mollo; Adam Dahlgren Lindström; Hannah Devinney; Virginia Dignum; Petter Ericson; Anna Jonsson; Timotheus Kampik; Tom Lenaerts; Julian Alfredo Mendez; Juan Carlos Nieves

doi:10.1145/3593013.3594059

ACROCPoLis: A Descriptive Framework for Making Sense of Fairness

Andrea Aler Tubella, Dimitri Coelho Mollo, Adam Dahlgren Lindström, Hannah Devinney, Virginia Dignum, Petter Ericson, Anna Jonsson, Timotheus Kampik, Tom Lenaerts, Julian Alfredo Mendez, Juan Carlos Nieves

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
Publisher	Association for Computing Machinery
Pages	1014-1025
Number of pages	12
ISBN (Electronic)	9781450372527
DOIs	https://doi.org/10.1145/3593013.3594059
Publication status	Published - 12 Jun 2023
Event	6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 - Chicago, United States Duration: 12 Jun 2023 → 15 Jun 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
Country/Territory	United States
City	Chicago
Period	12/06/23 → 15/06/23

Bibliographical note

Funding Information:
We would like to thank the anonymous reviewers for their thoughtful feedback, which has led us to substantially expand the discussion of ACROCPoLis in this paper.

Publisher Copyright:
© 2023 Owner/Author.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

Algorithmic fairness
responsible AI
social impact of AI
socio-technical processes

Access to Document

10.1145/3593013.3594059

Cite this

Aler Tubella, A., Coelho Mollo, D., Dahlgren Lindström, A., Devinney, H., Dignum, V., Ericson, P., Jonsson, A., Kampik, T., Lenaerts, T., Mendez, J. A., & Nieves, J. C. (2023). ACROCPoLis: A Descriptive Framework for Making Sense of Fairness. In Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 (pp. 1014-1025). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3593013.3594059

Aler Tubella, Andrea ; Coelho Mollo, Dimitri ; Dahlgren Lindström, Adam ; Devinney, Hannah ; Dignum, Virginia ; Ericson, Petter ; Jonsson, Anna ; Kampik, Timotheus ; Lenaerts, Tom ; Mendez, Julian Alfredo ; Nieves, Juan Carlos. / ACROCPoLis : A Descriptive Framework for Making Sense of Fairness. Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023. Association for Computing Machinery, 2023. pp. 1014-1025 (ACM International Conference Proceeding Series).

@inproceedings{1d19c20472d94eb7b872ae561fe58e8a,

title = "ACROCPoLis: A Descriptive Framework for Making Sense of Fairness",

abstract = "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.",

keywords = "Algorithmic fairness, responsible AI, social impact of AI, socio-technical processes",

author = "{Aler Tubella}, Andrea and {Coelho Mollo}, Dimitri and {Dahlgren Lindstr{\"o}m}, Adam and Hannah Devinney and Virginia Dignum and Petter Ericson and Anna Jonsson and Timotheus Kampik and Tom Lenaerts and Mendez, {Julian Alfredo} and Nieves, {Juan Carlos}",

note = "Funding Information: We would like to thank the anonymous reviewers for their thoughtful feedback, which has led us to substantially expand the discussion of ACROCPoLis in this paper. Publisher Copyright: {\textcopyright} 2023 Owner/Author. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.; 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 ; Conference date: 12-06-2023 Through 15-06-2023",

year = "2023",

month = jun,

day = "12",

doi = "10.1145/3593013.3594059",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "1014--1025",

booktitle = "Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023",

}

Aler Tubella, A, Coelho Mollo, D, Dahlgren Lindström, A, Devinney, H, Dignum, V, Ericson, P, Jonsson, A, Kampik, T, Lenaerts, T, Mendez, JA & Nieves, JC 2023, ACROCPoLis: A Descriptive Framework for Making Sense of Fairness. in Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 1014-1025, 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, United States, 12/06/23. https://doi.org/10.1145/3593013.3594059

ACROCPoLis : A Descriptive Framework for Making Sense of Fairness. / Aler Tubella, Andrea; Coelho Mollo, Dimitri; Dahlgren Lindström, Adam; Devinney, Hannah; Dignum, Virginia; Ericson, Petter; Jonsson, Anna; Kampik, Timotheus; Lenaerts, Tom; Mendez, Julian Alfredo; Nieves, Juan Carlos.

Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023. Association for Computing Machinery, 2023. p. 1014-1025 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

TY - GEN

T1 - ACROCPoLis

T2 - 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023

AU - Aler Tubella, Andrea

AU - Coelho Mollo, Dimitri

AU - Dahlgren Lindström, Adam

AU - Devinney, Hannah

AU - Dignum, Virginia

AU - Ericson, Petter

AU - Jonsson, Anna

AU - Kampik, Timotheus

AU - Lenaerts, Tom

AU - Mendez, Julian Alfredo

AU - Nieves, Juan Carlos

N1 - Funding Information: We would like to thank the anonymous reviewers for their thoughtful feedback, which has led us to substantially expand the discussion of ACROCPoLis in this paper. Publisher Copyright: © 2023 Owner/Author. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.

PY - 2023/6/12

Y1 - 2023/6/12

N2 - 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.

AB - 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.

KW - Algorithmic fairness

KW - responsible AI

KW - social impact of AI

KW - socio-technical processes

UR - http://www.scopus.com/inward/record.url?scp=85163594710&partnerID=8YFLogxK

U2 - 10.1145/3593013.3594059

DO - 10.1145/3593013.3594059

M3 - Conference paper

AN - SCOPUS:85163594710

T3 - ACM International Conference Proceeding Series

SP - 1014

EP - 1025

BT - Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023

PB - Association for Computing Machinery

Y2 - 12 June 2023 through 15 June 2023

ER -

Aler Tubella A, Coelho Mollo D, Dahlgren Lindström A, Devinney H, Dignum V, Ericson P et al. ACROCPoLis: A Descriptive Framework for Making Sense of Fairness. In Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023. Association for Computing Machinery. 2023. p. 1014-1025. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3593013.3594059

ACROCPoLis: A Descriptive Framework for Making Sense of Fairness

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Cite this