Explainable AI for Cheating Detection and Churn Prediction in Online Games

Online gaming is a multibillion dollar industry that entertains a large, global population. Empowering online games with AI has made a great success, however, ignores the explainability of black-box model makes AI less responsible and hinders its further development. In this article, we introduce and discuss the audience and the concept of XAI (eXplainable AI) in online games. We propose a GXAI workflow, which combines the strong expressiveness of multiview data sources and the clear transparency of multiview black-box models. We present four specific classifiers and explainers in the character portrait view, the behavior sequence view, the client image view, and the social graph view. Experiments conducted on real-world datasets for game cheating detection and player churn prediction show the accuracy of classification and the rationality of explanation. We also discover and present numerous interesting and valuable findings from the individual, local, and global explanations. We implement and deploy three practical applications, including evidence and reason generation, model debugging and testing, and model compression and comparison in NetEase Games and have received quite positive reviews from user studies. More future work is in progress since this is the first work that introduces XAI in online games.