Abstract
Fairness has emerged as a crucial topic in data mining and machine learning applications, driven by ethical and legal considerations. It is important to recognize that not all samples are treated unfairly, resulting in data heterogeneity in fair machine learning. Existing fair models primarily focus on achieving fairness across all heterogeneous data, yet they often fall short in ensuring fairness within specific subgroups, such as fairly treated and unfairly treated data. This paper presents a novel problem of training a fair model on heterogeneous data, aiming to achieve fairness for both types of data, with a particular emphasis on the unfairly treated subset. To address this challenge, an effective approach is to recover the distribution of both fairly and unfairly treated data. In this study, we adopt the Structural Causal Model (SCM) to model the heterogeneous data as a mixture of causal structures. Leveraging the perspective of SCM, we propose a framework called FairDR, which utilizes the Hirschfeld-Gebelein-Rényi (HGR) correlation to accurately recover the distribution of both fairly and unfairly treated data. FairDR can serve as a pre-processing method for other fair machine learning models, providing protection for the unfairly treated members. Through empirical evaluation on synthetic and real-world datasets, we demonstrate that the presence of heterogeneous data can introduce unfairness in previous algorithms. However, FairDR successfully recovers the distribution of fairly and unfairly treated data, thus improving the fairness of downstream algorithms when dealing with heterogeneous data.
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Acknowledgement
This work was supported in part by Zhejiang Province Natural Science Foundation (LQ21F020020), National Natural Science Foundation of China (62006207, U20A20387), Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), and the Fundamental Research Funds for the Central Universities (226-2022-00142, 226-2022-00051).
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Liu, Y., Kuang, K., Zhang, F., Wu, F. (2024). FairDR: Ensuring Fairness in Mixed Data of Fairly and Unfairly Treated Instances. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_1
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