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Graph neural networks are powerful graph representation learners in which node representations are highly influenced by features of neighboring nodes. Prior work on individual fairness in graphs has focused only on node features rather than structural issues. However, from the perspective of fairness in high-stakes applications, structural fairness is also important, and the learned representations may be systematically and undesirably biased against unprivileged individuals due to a lack of structural awareness in the learning process. In this work, we propose a pre-processing bias mitigation approach for individual fairness that gives importance to local and global structural features. We mitigate the local structure discrepancy of the graph embedding via a locally fair PageRank method. We address the global structure disproportion between pairs of nodes by introducing truncated singular value decomposition-based pairwise node similarities. Empirically, the proposed pre-processed fair structural features have superior performance in individual fairness metrics compared to the state-of-the-art methods while maintaining prediction performance. 

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence—they are critical for both persisting and thriving in an uncertain future.