Reliability & Safety Engineering

AI models are powerful but unreliable in ways that are fundamentally different from traditional software. A conventional programme either works as designed or throws an error - AI models can fail silently, producing plausible-sounding but completely wrong outputs with no error message and no indication that anything went wrong. This category covers the engineering practices and techniques for making AI systems more reliable, predictable and safe in production. Hallucination, calibration, adversarial robustness and drift are all challenges that any serious AI deployment must address. These aren't theoretical concerns - they're the issues that cause real harm: a legal AI that invents case citations, a medical AI that gives dangerous advice confidently, a customer service AI that makes promises your company can't keep. The good news is that practical mitigations exist for all of these problems. The bad news is that none of them fully solve the underlying issues - they reduce risk rather than eliminate it. For businesses deploying AI, reliability and safety engineering should receive material attention and investment - a reliable system built on a modest model will serve you better than a brilliant model deployed without guardrails.