A few months ago, I met the founder of a company in the recruiting business. They aggregate online profiles of people, both applicants and other people happily employed at their current jobs. Then, based on the combination of their LinkedIn, StackOverflow, Facebook, FourSquare etc, their algorithm ranks folks according to the desired characteristics for a given position.
It sounds interesting in theory. That is, until I got to asking about how they quantified softer qualities that employers look for, like an applicant's social skills or potential for a client facing role. Apparently, to determine this, they look at the number of "check ins" people do at locations that are not their home city while employed. Their algorithm assumes that the person is traveling for business and is therefore trusted to meet customers.
There are so many assumptions in this one example that it makes me question the integrity of the whole system. An algorithm is only as good as the person designing it. Maybe Evolv really is better than these guys at finding quantitative markers for softer skills, but I remain skeptical.
And what if you have 2/4 of those? Or have their privacy to max status? How do they account for those? Like you said, the algorithm is only as good as the writer.
Actually, I asked about that, since my privacy settings is at the max on Facebook and maybe I used FourSquare once? I also never anchor Facebook posts to a location.
His response was basically that they are "trusted partner" (quotes because I can't remember the exact term, but that sounds right) of Facebook and so they get all the data somehow. Maybe they pay for it? I am not sure how it would work because it couldn't be anonymized for their service.
Anyway, he basically said that, when you combine the Facebook location based posts and FourSquare checkins (and probably location anchored Tweets too as well as others I am missing), there are so many millions of these happening everyday that some of those people are bound to be qualified for the position a company is hiring for.
...Which brings us back to problematic assumptions. Garbage in, garbage out.
Well now, I was unaware FB was selling my data in this way. Looking back, it makes sense, but still, that's a little chilly to me.
But yes, you end up selecting people that:
1) Have smart-phones
2) Use them, and not have their kids mess with them
3)Use many of these services
4)Are not caught up in the SV non-compete bubble
5)Are good enough nonetheless
6)Are looking
This limits the use of this idea incredibly. Not to mention all the regular Federal issues of race, sex, creed, and orientation. If I were hiring, unless this were incredibly cheap, I'd stay far away from it. It's just plain creepy.
It sounds interesting in theory. That is, until I got to asking about how they quantified softer qualities that employers look for, like an applicant's social skills or potential for a client facing role. Apparently, to determine this, they look at the number of "check ins" people do at locations that are not their home city while employed. Their algorithm assumes that the person is traveling for business and is therefore trusted to meet customers.
There are so many assumptions in this one example that it makes me question the integrity of the whole system. An algorithm is only as good as the person designing it. Maybe Evolv really is better than these guys at finding quantitative markers for softer skills, but I remain skeptical.