Intermediate · Safety

Privacy

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Protecting personal information that flows into or out of an AI system.

Technical Definition

Protecting personal information that flows into or out of an AI system.

How it works

Privacy concerns arise at training (was personal data scraped?), at inference (do prompts leak secrets?), and in outputs (does the model regurgitate private text?). Mitigations include data minimization, on-device inference, encryption, differential privacy, and clear retention policies.

Related Concepts

• AI Safety & Alignment — The field ensuring AI systems behave as intended, remain under human control, and avoid unintended harm.
• Differential Privacy — A mathematical framework that bounds how much any single individual's data can affect a model or query result.
• Data Leakage — When information that wouldn't be available at inference time accidentally influences training.