{"type":"ScholarlyArticle","title":"Towards Gender Fairness for Mental Health Prediction","authors":[{"name":"Jiaee Cheong","slug":"jiaee-cheong","given":"Jiaee","family":"Cheong","affiliation":"University of Cambridge; The Alan Turing Institute"},{"name":"Selim Kuzucu","slug":"selim-kuzucu","given":"Selim","family":"Kuzucu","affiliation":"Middle East Technical University"},{"name":"Sinan Kalkan","slug":"sinan-kalkan","given":"Sinan","family":"Kalkan","affiliation":"Middle East Technical University"},{"name":"Hatice Gunes","slug":"hatice-gunes","given":"Hatice","family":"Gunes","affiliation":"University of Cambridge"}],"abstract":"Mental health is becoming an increasingly prominent health challenge. Despite a plethora of studies analysing and mitigating bias for a variety of tasks such as face recognition and credit scoring, research on machine learning (ML) fairness for mental health has been sparse to date. In this work, we focus on gender bias in mental health and make the following contributions. First, we examine whether bias exists in existing mental health datasets and algorithms. Our experiments were conducted using Depresjon, Psykose and D-Vlog. We identify that both data and algorithmic bias exist. Second, we analyse strategies that can be deployed at the pre-processing, in-processing and post-processing stages to mitigate for bias and evaluate their effectiveness. Third, we investigate factors that impact the efficacy of existing bias mitigation strategies and outline recommendations to achieve greater gender fairness for mental health. Upon obtaining counter-intuitive results on D-Vlog dataset, we undertake further experiments and analyses, and provide practical suggestions to avoid hampering bias mitigation efforts in ML for mental health.","doi":"10.24963/ijcai.2023/658","url":"https://ijcai.org/proceedings/2023/658","pdf":"https://ijcai.org/proceedings/2023/0658.pdf","markdown":"https://ijcai.org/proceedings/2023/658.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/658","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 for Good","track":"AI for Good","number":658,"pages":{"first":5932,"last":5940},"keywords":["AI for Good: AI Ethics, Trust, Fairness","AI for Good: Multidisciplinary Topics and Applications","AI for Good: Humans and AI"],"videos":[],"references":[{"position":1,"raw":"[Bailey and Plumbley, 2021] Andrew Bailey and Mark D Plumbley. 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