Corrections

When a person overrides an AI system, they hand it something valuable and usually throw it away. The override is a labeled example. It says: here is a case, here is what the system produced, and here is the answer a person was willing to stand behind instead. That is the exact shape of the data used to train and evaluate these systems in the first place. It arrives for free, generated by the normal work of people doing their jobs, and it points at the precise spots where the system and reality disagree.

Consider what an override actually contains. It has the full input the system saw. It has the output the system chose. It has the corrected output, produced by someone who knew enough to disagree and cared enough to act. And it carries context that no synthetic test case has: it happened on a real case that mattered enough for a person to stop and fix. A test set assembled in advance guesses at where the system will fail. An override is a report of where it did fail, filed by the person who caught it.

One override is an anecdote. The value shows up when they are grouped. Sort a season of overrides and patterns appear that no single correction reveals. A cluster of overrides on the same kind of case means the system holds a rule that has quietly drifted out of date, and the people doing the work have been silently compensating for it. A cluster tied to one category, one region, one product line means cases are being mis-routed, sent to a path built for a different situation. A wide scatter of overrides with no pattern, spread across everything, means something else: this is a decision the model should probably never have been trusted with, because even its non-overridden answers are likely wrong at a similar rate and nobody caught them.

That last case is the one worth dwelling on. An override rate is not only a measure of how often the system is wrong. It is a measure of how often the system is wrong and a person notices. The cases nobody overrode are not all correct. Some are wrong and went out anyway, because the reviewer was busy, or trusted the system, or could not tell. So the observed override rate is a floor on the true error rate, not the rate itself. A decision where people override constantly and still miss some is a decision that does not belong to the system yet. The corrections are telling you that plainly, if anyone reads them.

Most systems do not read them. They log overrides, because logging is cheap and the field was already in the schema. Then they query that log for nothing. It becomes an audit trail that gets opened when something goes wrong and a manager wants to see who changed what. The signal that could improve the system sits in a table, complete and ignored, because using it would mean someone had to decide to look, and looking was nobody's job.

Reading corrections does not require retraining anything. Grouping them and counting is enough to surface a drifted rule or a mis-routed category, and those are findings a person can act on directly by fixing the rule or changing the routing. The corrections also make a natural evaluation set, better than one written in advance, because it is drawn from real cases and weighted toward exactly the situations where the system struggles. When a change is proposed, the honest test is whether it would have produced the corrected answer on the cases people actually corrected.

There is a reason this signal is so strong and so neglected at once. It is strong because it is expensive to produce: a knowledgeable person spent attention to create each example, which is precisely what makes labeled data costly to buy. It is neglected because it arrives as exhaust, a byproduct of normal work, and things that arrive as exhaust are easy to treat as waste. The correction is the most concentrated information the system generates about its own limits. A person already paid for it. Whether it does any good after that depends entirely on whether anything ever reads it back.