We don't know if Candidate 2 really "knows A, B, C, D, E and F by heart", just that they claim to. They could be adding whatever to their skill list just, even though they hardly used it, just because it' a buzzword.
So Candidate 1 could still blow them out of the water in performance, and even be able to trivially learn D, and E in a short while on the job if needed.
The skill vector wont tell much by itself, and even prevent finding the better candidate if its used for screening.
> We don't know if Candidate 2 really "knows A, B, C, D, E and F by heart", just that they claim to. They could be adding whatever to their skill list just, even though they hardly used it, just because it' a buzzword.
That is indeed a problem. I have been thinking about a possible solution to the very same problem for a while.
The fact: people lie on their resumes, and they do it for different reasons. There are white lies (e.g. pumps something up because they aspire to something but were not presented with an opportunity to do it, yet they are eager to skill themselves up, learn and do it, if given an opportunity). Then there are other lies. Generally speaking, lies are never black or white, true or false; they are a shade of grey.
So the best idea I have been able to come up with so far is a hybrid solution that entails the text embeddings (the skills similarity match and search) coupled with the sentiment analysis (to score the sincerity of the information stated on a resume) to gain an extra insight into the candidate's intentions. Granted, the sentiment analysis is an ethically murky area…
Sincerity score on a resume? I can't tell if you're joking or not. I mean yeah, any sentence that ends in something like "...yeah, that's the ticket." would be detectable for sure, but I'm not sure everyone is as bad a liar as Jon Lovitz.
Are you speaking hypothetically or from your own experience? The sentiment analysis is a thing, and it mostly works – I have tested it with satisfactory results on sample datasets. It is relatively easy to extract the emotional context from a corpus of text, less so when it comes to resumes due to their inherently more condensed content. Which is precisely why I mentioned ethical considerations in my previous response. With the extra effort and fine tuning, it should be possible to overcome most of the false negatives though.
Sure AI can detect emotional tones (being positive, being negative, even sarcasm sometimes) in writing, so if you mean something like detecting negativity in a resume so it can be thrown immediately in the trash, then I agree that can work. Any negative emotionality is always a red-flag.
But insofar as detecting lies in sentences, that simply cannot be done, because even if it ever did work the failure rate would still be 99%, so you're better off flipping a coin.
We don't know if Candidate 2 really "knows A, B, C, D, E and F by heart", just that they claim to. They could be adding whatever to their skill list just, even though they hardly used it, just because it' a buzzword.
So Candidate 1 could still blow them out of the water in performance, and even be able to trivially learn D, and E in a short while on the job if needed.
The skill vector wont tell much by itself, and even prevent finding the better candidate if its used for screening.