Teaching AI to Read Consumer Law: Evaluating Modern Language Models for Semantic Search

Machine learning (ML) is increasingly recognized as a powerful tool for scientific discovery, yet practical guidance for working with small to medium-sized datasets is limited. This article fills that gap by presenting lessons drawn directly from the author’s hands-on experience across natural and social sciences, including biomedical signal analysis, survey research, and behavioral prediction. Unlike a literature review, it reflects real-world applications, illustrating what works in practice and what pitfalls to avoid.

The study outlines a structured ML workflow emphasizing careful data preparation, model selection, rigorous validation, and interpretability. Both shallow and deep models are considered, with advanced techniques such as SHAP used to reveal how models make decisions and extract meaningful insights.

Results highlight that effective ML depends less on algorithmic complexity and more on disciplined methodology. Interpretable models, integration with domain knowledge, and thoughtful validation often outperform more sophisticated alternatives on modest datasets. Common challenges—including data leakage, default-model overreliance, and misconceptions of ML as an automatic solution—are addressed with practical examples.

This article serves as a guide for researchers who want to apply ML responsibly and effectively, demonstrating how real-world experience can transform small or imperfect datasets into scientifically meaningful insights.