The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei
Abstract
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security.
Topics & Concepts
Counterfactual thinkingRecommender systemExploitComputer scienceGreat RiftBlack boxArtificial intelligenceData scienceComputer securityMachine learningPsychologyAstronomyPhysicsSocial psychologyExplainable Artificial Intelligence (XAI)Machine Learning in HealthcareTopic Modeling