Unmasking the Clever Hans effect in AI models: shortcut learning, spurious correlations, and the path toward robust intelligence

The Clever Hans (CH) effect is a historical analogy of a horse solving mathematical problems based on some cues, representing a critical failure in artificial intelligence (AI) systems, where models achieve higher performance by utilizing spurious correlations and artifacts presented in the datasets rather than relying on causal relationships or task-related features. This effect or phenomenon is prevalent across multiple domains of AI such as computer vision, natural language processing, medical imaging, and reinforcement learning. This review examines the Clever Hans effect, the conceptual foundation of spurious correlations, and current evaluation methods that obscure such behavior. We further survey state-of-the-art detection and mitigation strategies, focusing on both model-centric and data-centric techniques. Building on these insights, we propose a roadmap for robust AI development, which includes standard benchmarking, causal integration, human-in-the-loop auditing, and transparent policy frameworks. This study underscores that addressing the Clever Hans effect is not only necessary for technical robustness but also for the ethical and responsible deployment of AI systems in real-world, high-stakes environments.