The paper "Unmasking Clever Hans predictors and assessing what machines really learn" by Sebastian Lapuschkin et al. explores the limitations of current machine learning (ML) models and proposes methods to explain their decision-making processes. The authors highlight that while ML models have achieved high accuracy in various tasks, they often exhibit behaviors that are not easily interpretable, such as "Clever Hans" phenomena, where the model relies on spurious correlations rather than true underlying patterns. They introduce the Spectral Relevance Analysis (SpRay) method, which uses spectral clustering to identify and characterize the decision-making strategies of ML models. This method helps in understanding whether the learned strategies are valid, strategic, or based on spurious correlations. The paper demonstrates the effectiveness of SpRay through examples in computer vision and arcade games, showing that standard performance metrics can fail to distinguish between different problem-solving behaviors. The authors argue that explaining the decisions of ML models is crucial for assessing their reliability and generalizability, and they provide a semi-automated tool to facilitate this process. The work aims to add a voice of caution to the excitement surrounding machine intelligence and to promote a more nuanced evaluation of recent successes in ML.The paper "Unmasking Clever Hans predictors and assessing what machines really learn" by Sebastian Lapuschkin et al. explores the limitations of current machine learning (ML) models and proposes methods to explain their decision-making processes. The authors highlight that while ML models have achieved high accuracy in various tasks, they often exhibit behaviors that are not easily interpretable, such as "Clever Hans" phenomena, where the model relies on spurious correlations rather than true underlying patterns. They introduce the Spectral Relevance Analysis (SpRay) method, which uses spectral clustering to identify and characterize the decision-making strategies of ML models. This method helps in understanding whether the learned strategies are valid, strategic, or based on spurious correlations. The paper demonstrates the effectiveness of SpRay through examples in computer vision and arcade games, showing that standard performance metrics can fail to distinguish between different problem-solving behaviors. The authors argue that explaining the decisions of ML models is crucial for assessing their reliability and generalizability, and they provide a semi-automated tool to facilitate this process. The work aims to add a voice of caution to the excitement surrounding machine intelligence and to promote a more nuanced evaluation of recent successes in ML.