I think that you need to consider both precision and recall of your interview process. The standard interview process is optimized for precision—you want to be as sure as possible that the people you identify as good are actually good. This is in part because it’s very expensive to fix a hiring mistake, and also because the candidate pool is very bad. The good candidates get hired and keep jobs, and the bad candidates keep interviewing.
If you come up with a new process that has higher recall (can find Bob when the typical process doesn’t), unless you’ve invented something that dominates the typical process, you’re going to get a bunch of false positives and end up hiring people you think are Bobs but are actually bad.
TL;DR your post focuses on recall (avoiding false negatives) but in reality precision (avoiding false positives) is much more important because the candidate pool is mostly terrible.
I think that you need to consider both precision and recall of your interview process. The standard interview process is optimized for precision—you want to be as sure as possible that the people you identify as good are actually good. This is in part because it’s very expensive to fix a hiring mistake, and also because the candidate pool is very bad. The good candidates get hired and keep jobs, and the bad candidates keep interviewing.
If you come up with a new process that has higher recall (can find Bob when the typical process doesn’t), unless you’ve invented something that dominates the typical process, you’re going to get a bunch of false positives and end up hiring people you think are Bobs but are actually bad.
TL;DR your post focuses on recall (avoiding false negatives) but in reality precision (avoiding false positives) is much more important because the candidate pool is mostly terrible.