Trust Issues: Why Neural Networks Need Their Own Internal Affairs Department
Accuracy is a comforting number. That is precisely the problem. A neural network can score well on a test set and still be operationally suspicious. The labels may be corrupted. The input may be degraded. A small patch may have quietly hijacked part of the model’s learned behavior. The model may be confident, calibrated enough for a dashboard, and still untrustworthy in the one place where the business actually needs it to behave. ...