AI Reliability Education
3 articles
AI reliability concerns whether a system behaves consistently and accurately enough for a particular purpose and level of risk. Topics include hallucinated claims, inconsistent responses, uncertainty, factual verification, and failures that appear only under some conditions; evaluation methods must account for the task, the data used, and the consequences of error. Meaningful reliability work defines observable failure cases, tests representative inputs, and documents when human review is needed.