Model Calibration

Confidence is cheap. A classifier can always give you a probability. The awkward question is whether that probability deserves to be believed. This is not a philosophical problem when the model is recommending a movie. It becomes expensive when the model is screening documents, triaging support tickets, flagging fraud, routing legal clauses, or deciding whether a case should be escalated to a human. In those settings, “92% confident” is not decoration. It is an operating instruction. ...

A hospital does not need a chatbot that sounds certain. It needs a system that knows when certainty would be irresponsible. That sounds obvious until one remembers how most AI demos behave: fluent answer first, caveat somewhere after the damage has already put on shoes. In clinical decision support, this is not a stylistic defect. It is an operating risk. A model can be wrong in many ways, but the most dangerous version is the confidently wrong one: the triage answer that should have been escalated, the medication suggestion that should have been checked, the risk score that looks clean only because the system has no vocabulary for doubt. ...

TL;DR for operators LoRA and prompts are attractive because they make model adaptation feel almost too easy: add a few examples, attach a small adapter, nudge the model into a domain, and call it customised. The uncomfortable part is that adaptation changes not only what a model says, but how confidently it says it. A compliance assistant that becomes slightly more domain-specific but far more overconfident has not been improved. It has been promoted beyond its competence, a classic corporate move. ...