I like this because it’s simple and obviously correct. Also I can see at least one way you could implement it:
a. Suppose the AI is ‘shadowing’ a human worker doing a critical task. Say it is ‘shadowing’ a human physician.
b. Each time the AI observes the same patient, it regresses between [data from the patient] and [predicted decision a ‘good’ physician would make, predicted outcome for the ‘good’ decision]. Once the physician makes a decision and communicates it, the AI regresses between [decision the physician made] and [predicted outcome for that decision].
c. The machine also must have a confidence or this won’t work.
With large numbers and outright errors made by the physician, it’s then possible to detect all the cases where the [decision the physician made] has a substantially worse outcome than the [predicted decision a ‘good’ physician would make], and when the AI has a high confidence of this [requiring many observations of similar situations] and it’s time to call for a second opinion.
In the long run, of course, there will be a point where the [predicted decision a ‘good’ physician would make] is better than the [information gain from a second human opinion] and you really would do best by firing the physician and having the AI make the decisions from then on, trusting for it to call for a second opinion when it is not confident.
(as an example, alpha go zero likely doesn’t benefit from asking another master go player for a ‘second opinion’ when it sees the player it is advising make a bad call)
I like this because it’s simple and obviously correct. Also I can see at least one way you could implement it:
a. Suppose the AI is ‘shadowing’ a human worker doing a critical task. Say it is ‘shadowing’ a human physician.
b. Each time the AI observes the same patient, it regresses between [data from the patient] and [predicted decision a ‘good’ physician would make, predicted outcome for the ‘good’ decision]. Once the physician makes a decision and communicates it, the AI regresses between [decision the physician made] and [predicted outcome for that decision].
c. The machine also must have a confidence or this won’t work.
With large numbers and outright errors made by the physician, it’s then possible to detect all the cases where the [decision the physician made] has a substantially worse outcome than the [predicted decision a ‘good’ physician would make], and when the AI has a high confidence of this [requiring many observations of similar situations] and it’s time to call for a second opinion.
In the long run, of course, there will be a point where the [predicted decision a ‘good’ physician would make] is better than the [information gain from a second human opinion] and you really would do best by firing the physician and having the AI make the decisions from then on, trusting for it to call for a second opinion when it is not confident.
(as an example, alpha go zero likely doesn’t benefit from asking another master go player for a ‘second opinion’ when it sees the player it is advising make a bad call)