Strategic Classification with Graph Neural Networks

Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of learning with more elaborate models that break the independence assumption. Motivated by the idea that applications of strategic classification are often social in nature, we focus on graph neural networks, which make use of social relations between users to improve predictions. Using a graph for learning introduces inter-user dependencies in prediction; our key point is that strategic users can exploit these to promote their own goals. As we show through analysis and simulation, this can work either against the system--or for it. Based on this, we propose a differentiable framework for strategically-robust learning of graph-based classifiers. Experiments on several real networked datasets demonstrate the utility of our approach. Machine learning is increasingly being used to inform decisions about humans. But when users of a system stand to gain from certain predictive outcomes, they may be prone to "game" the system by strategically modifying their features (at some cost). The literature on strategic classification (Brückner & Scheffer, 2011; Hardt et al., 2016) studies learning in this setting, with emphasis on how to learn classifiers that are robust to strategic user behavior. The idea that users may respond to a decision rule applies broadly and across many domains, from hiring, admissions, and scholarships to loan approval, insurance, welfare benefits, and medical eligibility (McCrary, 2008; Almond et al., 2010; Camacho & Conover, 2011; Lee & Lemieux, 2010). But despite these advances, most works in strategic classification remain to follow the original problem formulation in assuming independence across users responses. From a technical perspective, this assumption greatly simplifies the learning task, as it allows the classifier to consider each user's response in isolation: user behavior is modeled via a response mapping Intuitively, a user will modify her features if this'moves' her across the decision boundary, as long as this is worthwhile (i.e., gains from prediction exceed modification costs).