The Data Deficit: What’s Behind AI’s Worst Flaws

This white paper explores how AI's most critical flaws, from biased decision-making to safety failures originate at the data layer rather than in the code itself. Based on massive experience on AI moderation, the paper highlights how a lack in data quality, diversity, and human oversight can lead to real-world consequences. By shifting focus from data quantity to intentional design and human-in-the-loop review, organizations can bridge the gap between innovation and safety.