How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes

Recent insights highlight how metrics and large language models (LLMs) can produce paradoxical or misleading results, emphasizing the importance of understanding their limitations. These paradoxes reveal that numerical indicators and AI outputs may not always accurately reflect underlying realities, underscoring the need for critical evaluation and robust validation methods in data science and AI applications.