Unmasking the Clever Hans Effect in AI Models: Shortcut Learning, Spurious Correlations and Towards Robust Intelligence

Abhay Kumar Pathak^1*Manjari Gupta^1*Garima Jain^2*

• ¹Banaras Hindu University Faculty of Science, Varanasi, India
• ²MIRNOW, Varanasi, India

Clever Hans effect is a historical analogy of a horse solving mathematical problems based on some cues, representing the critical failure in AI systems where models achieve higher performance by utilizing spurious correlations and artifacts presented in the datasets, instead of using causal relationship 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, conceptual foundation of spurious correlations along with current evaluation methods that obscure such behaviour. We further survey the 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 to robust AI development, which includes standard benchmarking, causal integration, human-in-the-loop auditing and transparent policy frameworks. This work underscores the fact that addressing of Clever Hans effect is not only necessary for technical robustness but also for the ethical and responsible deployment of AI systems in a real-world high-stake environments.