Imagine an AI that excels at identifying birds, but only when they're perched on a specific type of branch. Or a medical AI that diagnoses diseases accurately, but only when a certain medical instrument is visible in the scan. These scenarios illustrate the challenge of "spurious correlations" in AI, where models latch onto irrelevant shortcuts instead of true diagnostic features. Introducing LADDER, a groundbreaking approach that uses the power of language to uncover and rectify these hidden biases in AI models. Unlike traditional methods that rely on manual attribute labeling or clustering, LADDER employs large language models (LLMs) to analyze image captions or radiology reports. By comparing the language used to describe correctly and incorrectly classified images, LADDER identifies potential biases. Think of it as a detective examining witness testimonies to pinpoint inconsistencies. LADDER then formulates hypotheses about these biases—for example, "the model mistakes objects in the background for key features"—and tests them by evaluating model performance on subsets of data where the suspected attributes are present or absent. This language-driven approach allows LADDER to capture complex relationships and subtle biases that traditional methods miss, even identifying nuances in medical images like the type or extent of a condition. Once LADDER pinpoints the biases, it generates "pseudo-labels" for the problematic attributes. These pseudo-labels help retrain the model, effectively correcting the learned shortcuts without needing manual annotations. Experiments across diverse datasets, from bird classification to medical diagnoses, show LADDER consistently outperforms existing methods in both bias detection and mitigation. LADDER represents a significant step toward building more robust and reliable AI systems. By leveraging the richness of human language, LADDER unlocks the blind spots in AI perception, helping models see the world more accurately and make more informed decisions. While the current version of LADDER relies on the quality of captions and vision-language models, future research aims to further refine this process, potentially even eliminating the need for textual data altogether. This promises an even more powerful tool for debiasing AI and ensuring its responsible application across a wide range of fields.