Formal Verification

Imagine two fairness bugs. In the first, changing a protected attribute while holding everything else constant shifts a model’s output enough to trigger one unfair decision. In the second, the same underlying applicant profile can fracture into nineteen meaningfully different score bands as protected attributes change. A conventional pairwise fairness test records both as violations. One counterexample each. Very tidy. Also not especially useful. ...

How to Make Neural Networks Talk: Register Automata as Their Unexpected Interpreters Prices move. Sensors drift. Users click, pause, return, disappear, and sometimes behave exactly like a Markov chain with a caffeine problem. Modern sequence models are good at turning such streams into decisions. A recurrent network or transformer can look at a run of numbers and say: buy, flag, reject, approve, alert. What it usually cannot do is explain the rule it has learned in a form that a risk team, engineer, or auditor can actually inspect. ...

A booking agent is not dangerous because it can “reason.” It is dangerous because it can remember the wrong thing, forget the right thing, loop politely forever, or book the flight before the human has actually confirmed. The philosophy department may enjoy debating whether this counts as intention. The operations team has a simpler question: can we know, before deployment, what behaviours this system can produce? ...

TL;DR for operators Most enterprise AI failures do not come from the model being “too small”. They come from the system around the model being too vague. A model gives an answer. The workflow accepts it. Nobody knows whether the reasoning path was valid, whether the data path was stale, whether the tool should have been called, or whether the whole process should be redesigned after repeated mistakes. Then someone asks why the AI confidently did something expensive. Excellent. We have automated the intern, but forgot to hire the supervisor. ...