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1. InFoRM: Individual Fairness on Graph Mining

   https://doi.org/doi.org/10.1145/3394486.3403080  

   Jian Kang, Jingrui He (August 2020, KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data)

   null (Ed.)

   Algorithmic bias and fairness in the context of graph mining have largely remained nascent. The sparse literature on fair graph mining has almost exclusively focused on group-based fairness notation. However, the notion of individual fairness, which promises the fairness notion at a much finer granularity, has not been well studied. This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM). First, we present a generic definition of individual fairness for graph mining which naturally leads to a quantitative measure of the potential bias in graph mining results. Second, we propose three mutually complementary algorithmic frameworks to mitigate the proposed individual bias measure, namely debiasing the input graph, debiasing the mining model and debiasing the mining results. Each algorithmic framework is formulated from the optimization perspective, using effective and efficient solvers, which are applicable to multiple graph mining tasks. Third, accommodating individual fairness is likely to change the original graph mining results without the fairness consideration. We conduct a thorough analysis to develop an upper bound to characterize the cost (i.e., the difference between the graph mining results with and without the fairness consideration). We perform extensive experimental evaluations on real-world datasets to demonstrate the efficacy and generality of the proposed methods. 

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2. Individual Fairness with Group Constraints in Graph Neural Networks

   Wang, Zichong; Ulloa, David; Yu, Tongjia; Rangaswami, Raju; Yap, Roland; Zhang, Wenbin (October 2024, Proceedings of the 27th European Conference on Artificial Intelligence (ECAI), 2023)

   Graph Neural Networks (GNNs) have demonstrated remarkable capabilities across various domains. Despite the successes of GNN deployment, their utilization often reflects societal biases, which critically hinder their adoption in high-stake decision-making scenarios such as online clinical diagnosis, financial crediting, etc. Numerous efforts have been made to develop fair GNNs but they typically concentrate on either individual or group fairness, overlooking the intricate interplay between the two, resulting in the enhancement of one, usually at the cost of the other. In addition, existing individual fairness approaches using a ranking perspective fail to identify discrimination in the ranking. This paper introduces two innovative notions dealing with individual graph fairness and group-aware individual graph fairness, aiming to more accurately measure individual and group biases. Our Group Equality Individual Fairness (GEIF) framework is designed to achieve individual fairness while equalizing the level of individual fairness among subgroups. Preliminary experiments on several real-world graph datasets demonstrate that GEIF outperforms state-of-the-art methods by a significant margin in terms of individual fairness, group fairness, and utility performance. 

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3. iFiG: Individually Fair Multi-view Graph Clustering

   https://doi.org/10.1109/BigData55660.2022.10020548  

   Wang, Yian; Kang, Jian; Xia, Yinglong; Luo, Jiebo; Tong, Hanghang (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   In a connected world, fair graph learning is becoming increasingly important because of the growing concerns about bias. Yet, the vast majority of existing works assume that the input graph comes from a single view while ignoring the multi-view essence of graphs. Generally speaking, the bias in graph mining is often rooted in the input graph and is further introduced or even amplified by the graph mining model. It thus poses critical research questions regarding the intrinsic relationships of fairness on different views and the possibility of mitigating bias on multiple views simultaneously. To answer these questions, in this paper, we explore individual fairness in multi-view graph mining. We first demonstrate the necessity of fair multi-view graph learning. Building upon the optimization perspective of fair single-view graph mining, we then formulate our problem as a linear weighted optimization problem. In order to figure out the weight of each view, we resort to the minimax Pareto fairness, which is closely related to the Rawlsian difference principle, and propose an effective solver named iFiG that minimizes the utility loss while promoting individual fairness for each view with two different instantiations. The extensive experiments that we conduct in the application of multi-view spectral clustering and INFORM post-processing demonstrate the efficacy of our proposed method in individual bias mitigation. 

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4. Fair Graph Mining

   https://doi.org/10.1145/3459637.3482030  

   Kang, Jian; Tong, Hanghang (October 2021, CIKM)

   In today’s increasingly connected world, graph mining plays a piv- otal role in many real-world application domains, including social network analysis, recommendations, marketing and financial secu- rity. Tremendous efforts have been made to develop a wide range of computational models. However, recent studies have revealed that many widely-applied graph mining models could suffer from potential discrimination. Fairness on graph mining aims to develop strategies in order to mitigate bias introduced/amplified during the mining process. The unique challenges of enforcing fairness on graph mining include (1) theoretical challenge on non-IID nature of graph data, which may invalidate the basic assumption behind many existing studies in fair machine learning, and (2) algorith- mic challenge on the dilemma of balancing model accuracy and fairness. This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and (2) identify the open challenges and future trends. In particular, we start with reviewing the background, problem definitions, unique challenges and related problems; then we will focus on an in-depth overview of (1) recent techniques in enforcing group fairness, indi- vidual fairness and other fairness notions in the context of graph mining, and (2) future directions in studying algorithmic fairness on graphs. We believe this tutorial could be attractive to researchers and practitioners in areas including data mining, artificial intel- ligence, social science and beneficial to a plethora of real-world application domains. 

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5. A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning

   https://doi.org/10.3102/10769986231171711  

   Suk, Youmi; Han, Kyung_T (May 2023, Journal of Educational and Behavioral Statistics)

   As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to propose a new framework for algorithmic fairness based on differential item functioning (DIF), which has been commonly used to measure item fairness in psychometrics. Our fairness notion, which we call differential algorithmic functioning (DAF), is defined based on three pieces of information: a decision variable, a “fair” variable, and a protected variable such as race or gender. Under the DAF framework, an algorithm can exhibit uniform DAF, nonuniform DAF, or neither (i.e., non-DAF). For detecting DAF, we provide modifications of well-established DIF methods: Mantel–Haenszel test, logistic regression, and residual-based DIF. We demonstrate our framework through a real dataset concerning decision-making algorithms for grade retention in K–12 education in the United States. 

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