arXiv:2602.11802v2 Announce Type: replace Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods manipulate graph structures to mitigate prediction disparities, the topological biases inherent to social graphs remain poorly understood and are consistently conflated with homophily alone. In this work, we study the relationship between structural biases and fairness outcomes in LP. To this end, we formalize a taxonomy of to
As AI models are increasingly deployed in real-world, high-stakes applications like job recommendations, understanding and mitigating inherent biases is becoming a critical ethical and practical imperative.
Ensuring fairness in AI systems, especially those influencing social and economic outcomes, is paramount for public trust, regulatory compliance, and preventing the perpetuation or amplification of existing societal inequalities.
The focus on structural bias beyond simple homophily in link prediction suggests a more nuanced and sophisticated approach to identifying and addressing algorithmic unfairness, moving beyond surface-level interventions.
- · Ethical AI developers
- · Users of AI-driven recommendation systems
- · Social scientists
- · Regulators
- · Developers ignoring fairness principles
- · Organizations deploying biased AI without oversight
Improved fairness metrics and methodologies become standard practice in AI development.
Public awareness of algorithmic bias increases, leading to greater demand for transparent and equitable AI systems.
Enhanced understanding of structural biases could inform policy and create more equitable digital and real-world opportunities, but also risks being overlooked by profit-driven deployment.
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Read at arXiv cs.LG