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Background: Detecting exploitative or unethical player behavior in online gaming platforms is challenging due to ambiguous gray-area actions that are neither clearly legitimate nor illegal. Methods: explainability. Datasets from a massively multiplayer online role-playing game (MMORPG) spanning 88 days (49,739 player sessions) were augmented to address class imbalance. Anomaly detection using an Encoder-Decoder GAN for Anomaly Detection (EGBAD) approach generated anomaly-aware features. A stacked ensemble model combining Random Forest, XGBoost, and Artificial Neural Networks was developed, with SHAP and LIME providing explanations for predictions. Results: The proposed framework achieved 95.98% accuracy, 0.915 ROC-AUC, and 0.90 macro F1-score, outperforming baseline models. The integration of CTGAN improved minority class recall by 5-7 percentage points, while EGBAD-derived anomaly features enhanced gray-area detection. Human-in-the-loop triage for low-confidence predictions (6.8% of cases) achieved 75% human-AI agreement with reduced false positives (21% decrease) and false negatives (17% decrease). Discussion: The framework successfully balances automated detection with human oversight, providing transparent, interpretable decisions for player behavior moderation while maintaining fairness and reducing wrongful enforcement actions.
K. et al. (Fri,) studied this question.