Your Neighbor Matters: Towards Fair Decisions Under Networked Interference

In the era of big data, decision-making in social networks may introduce bias due to interconnected individuals. For instance, in peer-to-peer loan platforms on the Web, considering an individual's attributes along with those of their interconnected neighbors, including sensitive attributes, is vital for loan approval or rejection downstream. Unfortunately, conventional fairness approaches often assume independent individuals, overlooking the impact of one person's sensitive attribute on others' decisions. To fill this gap, we introduce "Interference-aware Fairness" (IAF) by defining two forms of discrimination as Self-Fairness (SF) and Peer-Fairness (PF), leveraging advances in interference analysis within causal inference. Specifically, SF and PF causally capture and distinguish discrimination stemming from an individual's sensitive attributes (with fixed neighbors' sensitive attributes) and from neighbors' sensitive attributes (with fixed self's sensitive attributes), separately. Hence, a network-informed decision model is fair only when SF and PF are satisfied simultaneously, as interventions in individuals' sensitive attributes or those of their peers both yield equivalent outcomes. To achieve IAF, we develop a deep doubly robust framework to estimate and regularize SF and PF metrics for decision models. Extensive experiments on synthetic and real-world datasets validate our proposed concepts and methods.