{"type":"ScholarlyArticle","title":"Fairness via Group Contribution Matching","authors":[{"name":"Tianlin Li","slug":"tianlin-li","given":"Tianlin","family":"Li","affiliation":"Nanyang Technological University , Singapore ,"},{"name":"Zhiming Li","slug":"zhiming-li","given":"Zhiming","family":"Li","affiliation":"Nanyang Technological University , Singapore ,"},{"name":"Anran Li","slug":"anran-li","given":"Anran","family":"Li","affiliation":"Nanyang Technological University , Singapore ,"},{"name":"Mengnan Du","slug":"mengnan-du","given":"Mengnan","family":"Du","affiliation":"New Jersey Institute of Technology , USA,"},{"name":"Aishan Liu","slug":"aishan-liu","given":"Aishan","family":"Liu","affiliation":"Beihang University , China,"},{"name":"Qing Guo","slug":"qing-guo","given":"Qing","family":"Guo","affiliation":"Institute of High Performance Computing (IHPC) , A*STAR , Singapore,; Centre for Frontier AI Research (CFAR) , A*STAR , Singapore ,"},{"name":"Guozhu Meng","slug":"guozhu-meng","given":"Guozhu","family":"Meng","affiliation":"SKLOIS , Institute of Information Engineering , Chinese Academy of Sciences , China,"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"Zhejiang Sci-Tech University , China; Nanyang Technological University , Singapore ,"}],"abstract":"Fairness issues in Deep Learning models have recently received increasing attention due to their significant societal impact. Although methods for mitigating unfairness are constantly proposed, little research has been conducted to understand how discrimination and bias develop during the standard training process. In this study, we propose analyzing the contribution of each subgroup (i.e., a group of data with the same sensitive attribute) in the training process to understand the cause of such bias development process. We propose a gradient-based metric to assess training subgroup contribution disparity, showing that unequal contributions from different subgroups are one source of such unfairness. One way to balance the contribution of each subgroup is through oversampling, which ensures that an equal number of samples are drawn from each subgroup during each training iteration. However, we have found that even with a balanced number of samples, the contribution of each group remains unequal, resulting in unfairness under the oversampling strategy. To address the above issues, we propose an easy but effective group contribution matching (GCM) method to match the contribution of each subgroup. Our experiments show that our GCM effectively improves fairness and outperforms other methods significantly.","doi":"10.24963/ijcai.2023/49","url":"https://ijcai.org/proceedings/2023/49","pdf":"https://ijcai.org/proceedings/2023/0049.pdf","markdown":"https://ijcai.org/proceedings/2023/49.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/49","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":49,"pages":{"first":436,"last":445},"keywords":["AI Ethics, Trust, Fairness: ETF: Fairness and diversity","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Alom et al., 2018] Md Zahangir Alom, Tarek M Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, and Vijayan K Asari. 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