AI Safety and Ethics

AI safety and ethics is the practice of building AI systems that behave as intended and treat people fairly. Safety covers preventing concrete harms — harmful outputs, misuse, systems acting beyond their mandate — while ethics covers the human questions: fairness, transparency, accountability, privacy, and consent. For LLM applications, the two converge on a practical question: what could this system do to a real person, and who answers for it when it does?

This isn’t a compliance checkbox you bolt on before launch. Every design decision you make as an AI engineer is a safety decision: which model you choose, what data flows into prompts, which tools an agent can call, whether a human reviews consequential actions. Models are trained on human text and inherit human biases; they hallucinate confidently; agents given tools can take real actions with real consequences. Regulation like the EU AI Act now assigns legal obligations by risk level, so “the model did it” is not a defense — you own your system’s behavior.

In practice, you’ll apply the principles this module has been building toward: define what your system must never do and encode it in guardrails, run adversarial testing before attackers do, keep humans in the loop for high-stakes decisions, document known limitations honestly, and monitor production behavior rather than assuming launch-day evaluation holds. The rest of this module’s techniques — moderation APIs, PII redaction, red teaming — are how these principles become code.