A novel explainable artificial intelligence framework using knockoffs techniques with applications to sports analytics

Abstract The rapid integration of black-box Machine Learning (ML) models into critical decision-making scenarios has triggered an urgent call for transparency from stakeholders in Artificial Intelligence (AI). This call stems from growing concerns about the deployment of models whose decisions lack justification, legitimacy, and detailed explanations of their behavior. To address these concerns, Explainable Artificial Intelligence (XAI) has emerged as a crucial field, focusing on methods and processes that enable the comprehension of how AI systems make decisions, generate predictions, and execute their functions. The importance of XAI lies in its ability to provide explanations that justify a model’s outputs, thereby ensuring trust and accountability in AI systems. In this work, we propose a novel XAI framework that leverages state-of-the-art statistical knockoff techniques to identify the most informative predictors while maintaining a controlled False Discovery Rate (FDR). This framework enhances informed decision-making by ensuring robust and interpretable insights. We validate our approach through synthetic data experiments, demonstrating that it can effectively identify important features with high power while providing finite-sample FDR control across various scenarios. We demonstrate the efficacy of our approach by applying it to predict the outcomes of National Football League (NFL) playoffs, a domain of significant importance in sports analytics. Our method provides invaluable insights that support strategic decision-making in the highly competitive field of professional football.