{"type":"ScholarlyArticle","title":"Ensuring Fairness Stability for Disentangling Social Inequality in Access to Education: the FAiRDAS General Method","authors":[{"name":"Eleonora Misino","slug":"eleonora-misino","given":"Eleonora","family":"Misino","affiliation":"University of Bologna"},{"name":"Roberta Calegari","slug":"roberta-calegari","given":"Roberta","family":"Calegari","affiliation":"University of Bologna"},{"name":"Michele Lombardi","slug":"michele-lombardi","given":"Michele","family":"Lombardi","affiliation":"University of Bologna"},{"name":"Michela Milano","slug":"michela-milano","given":"Michela","family":"Milano","affiliation":"University of Bologna"}],"abstract":"Recent advancements in Artificial Intelligence in Education (AIEd) have revolutionized educational practices using machine learning to extract insights from students' activities and behaviours. Performance prediction, a key domain within AIEd, aims to enhance student achievement levels and address sustainable development goals related to education, health, gender equality, and economic growth. However, the potential of AIEd to contribute to these goals is hindered by the lack of attention to fairness in prediction algorithms, leading to educational inequality. To address this gap, we introduce FAiRDAS a general framework that models long-term fairness as an abstract dynamic system.  Our approach, illustrated through a case study in AIEd with real data, offers a customizable solution to promote long-term fairness while promoting the stability of mitigation actions over time.","doi":"10.24963/ijcai.2024/820","url":"https://ijcai.org/proceedings/2024/820","pdf":"https://ijcai.org/proceedings/2024/0820.pdf","markdown":"https://ijcai.org/proceedings/2024/820.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/820","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":820,"pages":{"first":7412,"last":7420},"keywords":["AI Ethics, Trust, Fairness: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Aghaei et al., 2019] Sina Aghaei, Mohammad Javad Azizi, and Phebe Vayanos. 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