No, not really.
If you have an AI that can answer 90% of queries correctly AND now this is the key, it knows which 90% it can answer correctly, human in the loop can be incredibly valuable to answer that other 10%.
If you have an AI that can answer 90% of queries correctly AND now this is the key, it knows which 90% it can answer correctly, human in the loop can be incredibly valuable to answer that other 10%.
although I do have some ideas on how you could use vector similarity against past executions to get a 1-100 score on how likely a given action is to be approved rejected. You could set a dial to "anything below 60 just auto-reject it and provide the past feedback to the model preemptively". This would need a lot of experimentation, might even be a research angle (if it hasn't been tried already)
(thinking like cosine * {1 if approved, -1 if rejected} and normalize the score 1-100. You could maybe even weight rejection in 0 to -1 based on sentiment)