par Aler Tubella, Andrea;Coelho Mollo, Dimitri;Dahlgren Lindström, Adam;Devinney, Hannah;Dignum, Virginia;Ericson, Petter;Jonsson, Ana;Kampik, Timotheus;Lenaerts, Tom 
;Mendez, Julian Alfredo;Nieves, Juan Carlos
Référence 6th ACM Conference on Fairness, Accountability, and Transparency(6: 12/6/2023-15/06/2023: Chicago), Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Association for Computing Machinery, page (1014-1025)
Publication Publié, 2023-06-12
;Mendez, Julian Alfredo;Nieves, Juan CarlosRéférence 6th ACM Conference on Fairness, Accountability, and Transparency(6: 12/6/2023-15/06/2023: Chicago), Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Association for Computing Machinery, page (1014-1025)
Publication Publié, 2023-06-12
Publication dans des actes
| Résumé : | 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. |
