A Beginner's Guide to AI AI Training Data: Why Quantity Isn’t Enough
Feb 23, 2026
They unpack why massive datasets alone do not make AI reliable. Listeners hear why noisy, imbalanced data creates blind spots and can amplify bias. A notable fairness case study is discussed to show real-world pitfalls. Practical tips for auditing CRM and training data rounds out the conversation on building trustworthy AI.
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Quantity Boosts Capability But Amplifies Flaws
- Quantity increases capability by exposing models to more variation and edge cases.
- But scale also amplifies noise, bias and outdated information when data isn't filtered or balanced.
Noise Breaks Learning Even With More Data
- Models minimise loss across datasets so noisy labels cause them to learn spurious correlations.
- Adding low-quality data yields diminishing returns because signal-to-noise ratio falls and entropy increases.
Use Human Feedback To Inject Quality
- Use human feedback methods like RLHF, filtering and adversarial testing to inject curated judgment into models.
- Treat alignment as the stage where quality re-enters after raw scale reaches its limits.