[gtpasqual]

At first, I thought about this very negatively.

Then, I remembered how awful and idiotic most of my HR managers were. The parameters they used to select people were all based on their own biased upper-class background.

Now, an algorithm, if well-written, could grasp many other parameters that would be much more relevant. Innate and applicable abilities an HR manager would never consider.

[RogerL]

Okay, let's say you lose the genetic gamble, and you test poorly on these algorithms. I've seen no claim that they are are infallible, they just find correlations. If the correlation is not 1, it is measuring some people poorly (false negative).

So, you actually do quite well at work, but the test puts you in the low category. You are basically unhirable, or at only a fraction of the salary of your peers.

You end up with things like that recent Google hire thing that was all over social media - the author of the package for which they were hiring was declined because he didn't pass some whiteboard algorithm thingy. At least that was random - if he interviewed again he'd have a chance of getting hired. Tests like this would render a section of humanity unhirable.

At least now, even though I am subjected to randomness, I can eventually get a job and prove myself. Then I can take that proof and use it to get other jobs. Not in Silicon Valley as a programmer, perhaps, where they ignore your resume in favor of 'solve this 20 year problem in 30 minutes on a whiteboard while you pretend you didn't just study this and are working it through for the first time', but in the rest of the world.

[hobs]

I have worked for companies making hiring software, most HR people are swimming in a sea of applicants and still do something as dumb as a raw keyword search of resumes from their db and call people with almost no other reference.

That is if you are lucky and doing direct hire work, if you are looking for temporary work the question at the top of everyone's mind is how recently you have applied.

I remember seeing databases with 400k+ employees and they had maybe 200 active staff, always doing work trying to recruit more people instead of using the data they already had.