Demystifying the black box: an explainable AI framework for transparent detection of engine assistance in chess

The rapid democratization of superhuman chess engines has precipitated an integrity crisis in online chess. While traditional detection systems rely on statistical aggregation to flag illicit assistance, they often operate as opaque "black boxes," providing binary verdicts without accessible justification. This lack of transparency complicates the adjudication process and undermines player trust. This paper proposes a novel, hybrid Explainable AI (XAI) framework designed to detect and explain engine assistance. Utilizing a dataset of 1,000 games analyzed against Stockfish 16 at a fixed depth of 12, we engineered a feature set that captures non-linear cheating behaviors, including "Perfect Streaks" and complexity-dependent accuracy. These features trained a Gradient Boosting classifier that achieves high detection accuracy. Crucially, we integrate SHAP (SHapley Additive exPlanations) to identify global behavioral indicators and LIME (Local Interpretable Model-agnostic Explanations) to generate case-specific evidence for individual bans. Furthermore, we introduce an automated natural language generation layer that translates these complex XAI metrics into human-readable "Fair Play Reports." Our results demonstrate that this framework not only accurately identifies cheaters but also bridges the interpretability gap, providing arbiters with the transparent evidence required to validate automated decisions.