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1. Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection

   https://doi.org/10.1145/3459637.3482325  

   Burkholder, Kai; Kwock, Kenny; Xu, Yuesheng; Liu, Jiaxin; Chen, Chao; Xie, Sihong (October 2021, Proceedings of the 30th ACM International Conference on Information & Knowledge Management)

   Spamming reviews are prevalent in review systems to manipulate seller reputation and mislead customers. Spam detectors based on graph neural networks (GNN) exploit representation learning and graph patterns to achieve state-of-the-art detection accuracy. The detection can influence a large number of real-world entities and it is ethical to treat different groups of entities as equally as possible. However, due to skewed distributions of the graphs, GNN can fail to meet diverse fairness criteria designed for different parties. We formulate linear systems of the input features and the adjacency matrix of the review graphs for the certification of multiple fairness criteria. When the criteria are competing, we relax the certification and design a multi-objective optimization (MOO) algorithm to explore multiple efficient trade-offs, so that no objective can be improved without harming another objective. We prove that the algorithm converges to a Pareto efficient solution using duality and the implicit function theorem. Since there can be exponentially many trade-offs of the criteria, we propose a data-driven stochastic search algorithm to approximate Pareto fronts consisting of multiple efficient trade-offs. Experimentally, we show that the algorithms converge to solutions that dominate baselines based on fairness regularization and adversarial training. 

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2. Trade less Accuracy for Fairness and Trade-off Explanation for GNN

   https://doi.org/10.1109/BigData55660.2022.10020318  

   Liu, Yazheng; Zhang, Xi; Xie, Sihong (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved. 

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3. DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs

   https://doi.org/10.1109/IJCNN60899.2024.10650494  

   Jing, Shixiong; Chen, Lingwei; Li, Quan; Wu, Dinghao (June 2024, International Joint Conference on Neural Networks)

   As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, online reviews, and social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graph structures, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. In graph-based fraud detection, handling imbalanced datasets poses a significant challenge, as the minority class often gets overshadowed, diminishing the performance of conventional GNNs. While oversampling has recently been adapted for imbalanced graphs, it contends with issues such as graph heterophily and noisy edge synthesis. To address these limitations, this paper introduces DOS-GNN, incorporating Dual-feature aggregation with Over-Sampling to advance GNNs for class-imbalanced fraud detection on graphs. This model exploits feature separation and dual-feature aggregation to mitigate the impact of heterophily and acquire refined node embeddings that facilitate fraud oversampling to balance class distribution without the need for edge synthesis. Extensive experiments on four large and real-world fraud datasets demonstrate that DOS-GNN can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models. 

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4. Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

   Rolf, Esther; Simchowitz, Max; Dean, Sarah; Liu, Lydia T.; Bjorkegren, Daniel; Hardt, Moritz; Blumenstock, Joshua (April 2020, Proceedings of the 37th International Conference on Machine Learning)

   null (Ed.)

   While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts — online content recommendation and sustainable abalone fisheries — to underscore the generality of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare. 

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5. Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

   Rolf, Esther; Simchowitz, Max; Dean, Sarah; Liu, Lydia; Bjorkegren, Daniel; Hardt, Moritz; Blumenstock, Joshua E (January 2020, Proceedings of Machine Learning Research)

   null (Ed.)

   While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts — online content recommendation and sustainable abalone fisheries — to underscore the generality of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare. 

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