{"type":"ScholarlyArticle","title":"Extending Decision Tree to Handle Multiple Fairness Criteria","authors":[{"name":"Alessandro Castelnovo","slug":"alessandro-castelnovo","given":"Alessandro","family":"Castelnovo","affiliation":"Intesa Sanpaolo S.p.A , Italy; Dept. of Informatics, Systems & Communication , Univ. of Milan-Bicocca , Milan , Italy"}],"abstract":"The demand for machine learning systems that can provide both transparency and fairness is constantly growing. Since the concept of fairness depends on the context, studies in the literature have proposed various formalisation and mitigation strategies. In this work, we propose a novel, flexible, discrimination-aware classifier that allows the user to: (i) select and mitigate the desired fairness criterion from a set of available options; (ii) implement more than one fairness criterion; (iii) handle more than one sensitive attribute; and (iv) specify the desired level of fairness to meet specific business needs or regulatory requirements. Our approach is based on an optimised extension to the decision-tree classifier, and aims to provide transparent and fair rules to the final users.","doi":"10.24963/ijcai.2022/822","url":"https://ijcai.org/proceedings/2022/822","pdf":"https://ijcai.org/proceedings/2022/0822.pdf","markdown":"https://ijcai.org/proceedings/2022/822.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/822","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":822,"pages":{"first":5839,"last":5840},"keywords":["Machine Learning (ML): General","Philosophy and Ethics of AI (PEAI): General"],"videos":[],"references":[{"position":1,"raw":"[Barocas et al., 2017] Barocas, Hardt, and Narayanan. Fair- ness in machine learning. Nips tutorial, 1:2, 2017.","title":"Fairness in machine learning","authors":"Hardt Barocas, Narayanan","year":"2017","venue":"Nips tutorial","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Brieman et al., ] Brieman, Friedman, Olshen, and Stone. Classification and regression trees. Wadsworth Inc. [Castelnovo et al., 2021]","title":"Classification and regression trees","authors":"Brieman","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Castelnovo, Malandri, Mercorio, Mezzanzanica, and Cosentini. Towards fairness through time. In ECML PKDD, pages 647-663. Springer, 2021.","title":"Towards fairness through time","authors":"Malandri Castelnovo, Mercorio, Cosentini Mezzanzanica","year":"2021","venue":"ECML PKDD","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Kamiran et al., 2010] Kamiran, Calders, and Pechenizkiy. Discrimination aware decision tree learning. IEEE, 2010.","title":"Discrimination aware decision tree learning","authors":"Kamiran","year":"2010","venue":"IEEE","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Malandri et al., 2022] Malandri, Mercorio, Mezzanzanica, Nobani, and Seveso. ContrXT: Generating contrastive ex- planations from any text classifier. IF, 2022.","title":"ContrXT: Generating contrastive explanations from any text classifier","authors":"Malandri","year":"2022","venue":"IF","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Zafar et al., 2019] Zafar, Valera, Gomez-Rodriguez, and Gummadi. Fairness constraints: A flexible approach for fair classification. JMLR, 20(1):2737-2778, 2019.","title":"Fairness constraints: A flexible approach for fair classification","authors":"Valera Zafar, Gomez-Rodriguez, Gummadi","year":"2019","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Zhang and Ntoutsi, 2019] Zhang and Ntoutsi. Faht: an adaptive fairness-aware decision tree classifier. In AAAI, pages 1480-1486, 2019.","title":"Faht: an adaptive fairness-aware decision tree classifier","authors":"Ntoutsi ; Zhang, Ntoutsi Zhang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":"/proceedings/2019/205"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}