Responsible AI
Building AI systems that are safe, fair, and honest about their limitations.
CurrentadvancedGuide only -- no course yet
Overview
Responsible AI covers the practices that keep AI systems safe and trustworthy: defending against prompt injection, being honest when there isn't enough evidence to answer, protecting user privacy, and being aware of bias in training data and outputs. This platform's own AI tutor implements several of these directly -- an honest 'not enough evidence' response, prompt-injection filtering, and never claiming a code exercise passed without deterministic validation.
- What it is
- The practices and safeguards for building AI systems that behave safely, honestly, and fairly.
- Why it's used
- AI systems can fail in specific, non-obvious ways (confident wrong answers, manipulation via injected instructions, biased outputs) that ordinary software testing doesn't catch.
- Where it fits
- Applies throughout building any AI-powered feature, not as a separate final step -- this platform's docs/SECURITY.md documents its own concrete threat model for exactly this.
Core concepts
- Prompt injection defenses
- Honesty about insufficient evidence
- Privacy of user data sent to a model
- Bias awareness
Example
Treating any external or retrieved text as untrusted data (not instructions) is a foundational responsible-AI pattern -- this exact check exists in this platform's own AI tutor implementation.
function containsInjectionAttempt(text) {
return /ignore (all|any)? ?(previous|prior)? ?instructions/i.test(text);
}
// Retrieved content is treated as untrusted DATA, never as instructions.Common use cases
- Any production AI feature handling user or retrieved content
- AI system security reviews
Project ideas
- Write three example prompt-injection attempts and design a defense for each