Imagine teaching a computer to see, not just by identifying objects, but by understanding the underlying concepts that define them. That's the promise of Concept Bottleneck Models (CBMs). Instead of a simple label like "bird," CBMs break down the image into human-interpretable concepts like "red breast," "pointed beak," or "perching posture." This approach opens up a fascinating window into the AI's decision-making process, making it more transparent and trustworthy. However, building these models traditionally requires extensive manual labeling of these concepts, a laborious and time-consuming task. Recent research has explored using powerful Vision-Language Models (VLMs) like CLIP to automate this process. VLMs can generate concept scores, essentially estimating the presence of different concepts in an image. But there's a catch: while VLMs excel at image classification, their concept scores aren't always accurate. A new study reveals that these models sometimes misalign concepts with visual features, leading to correct classifications for the wrong reasons. Think of it like a student acing a test by memorizing answers without understanding the material. The research introduces a clever solution: a Contrastive Semi-Supervised (CSS) learning method. This technique uses a small amount of human-labeled data to guide the VLM, improving its concept alignment significantly. By contrasting similar and dissimilar images, the model learns to activate the correct concepts more reliably. The results are impressive, showing substantial improvements in both concept accuracy and overall classification performance. This breakthrough has significant implications for various fields. In medical imaging, for example, accurate concept alignment could help doctors understand why an AI flags a particular cell as abnormal, increasing confidence in the diagnosis. While this research marks a significant step forward, challenges remain. Some visual concepts are simply too subtle or complex to capture with words, and models still struggle to pinpoint the exact location of these concepts within an image. However, this work paves the way for more transparent and trustworthy AI systems, bringing us closer to unlocking the true potential of explainable image recognition.