I am in the middle of a little literature review on using machine learning for photo organisation and came across a statement that struck me as misconceived. The paper’s topic is segmenting photo streams into events and states at the end of page 5:
We believe that for end users, having a low miss rate is more valuable than having a low false alarm rate.
I believe this is a false assumption that will lead to frustrated end users. Out of my own experience I am convinced that the opposite is true.
They continue: “To correct a false alarm is a one-step process of removing the incorrect segment boundary. But to correct a miss, the user must ﬁrst realize that there is a miss, then figure out the position of the segment boundary.”
Similar to face detection users will be happy about a correct detection but unhappy about an algorithm that creates wrong boundaries they have to manually correct.
And if we assume, that a conservative algorithm still finds all the strong boundaries, the user might not miss the not detected boundaries after all.
Algorithms should not create new work for the user, but remove (some of) it.