We address the growing apprehension that GNNs, in the absence of fairness constraints, might produce biased decisions that disproportionately affect underprivileged groups or individuals. Departing from previous work, we introduce for the first time a method for incorporating the Gini coefficient as a measure of fairness to be used within the GNN framework. Our proposal, GRAPHGINI, works with the two different goals of individual and group fairness in a single system, while maintaining high prediction accuracy. GRAPHGINI enforces individual fairness through learnable attention scores that help in aggregating more information through similar nodes. A heuristic-based maximum Nash social welfare constraint ensures the maximum possible group fairness. Both the individual fairness constraint and the group fairness constraint are stated in terms of a differentiable approximation of the Gini coefficient. This approximation is a contribution that is likely to be of interest even beyond the scope of the problem studied in this paper. Unlike other state-of-the-art, GRAPHGINI automatically balances all three optimization objectives (utility, individual, and group fairness) of the GNN and is free from any manual tuning of weight parameters. Extensive experimentation on real-world datasets showcases the efficacy of GRAPHGINI in making significant improvements in individual fairness compared to all currently available state-of-the-art methods while maintaining utility and group equality.

翻译：我们解决了在缺乏公平约束条件下，GNN可能产生对弱势群体或个人造成不成比例影响的偏见决策这一日益增长的担忧。与以往工作不同，我们首次提出了一种将基尼系数作为公平性度量引入GNN框架的方法。我们的方案GRAPHGINI在保持高预测精度的同时，在同一系统中兼顾个体公平与群体公平两个不同目标。GRAPHGINI通过可学习的注意力分数强制执行个体公平性，这些分数有助于通过相似节点聚合更多信息。基于启发式的最大纳什社会福利约束确保实现最大可能的群体公平性。个体公平约束和群体公平约束均以基尼系数的可微近似形式表述。该近似方法的贡献很可能超越本文所研究问题的范畴。与其他现有技术不同，GRAPHGINI自动平衡GNN的所有三个优化目标（效用、个体公平和群体公平），无需任何手动调整权重参数。在真实数据集上的广泛实验表明，与现有所有方法相比，GRAPHGINI能在保持效用和群体平等的同时，显著提升个体公平性。