Sorry HR, your job descriptions suck

Monday, December 14, 2015

Machine Intelligence In The Real World

[...] Textio is a text editor that recommends improvements to job descriptions as you type. With it, I can go from a 40th percentile job description to a 90th percentile one in just a few minutes, all thanks to a beautifully presented machine learning algorithm.

I respect Human Resources professionals. Their job can be shitty. But so can their job descriptions. The prospects who know what you mean by “incumbent” are probably too pedantic and detail-oriented to apply to the likely underpaid and/or intellectually vapid position you’re hiring for. The ones who don’t know what you mean don’t actually know what they’re applying to, which makes them terrible prospects.

If machine learning can remedy that, I hope it gains wider use. But I don’t think machine learning is necessary to stop writing the kind of drivel that passes for a job description these days. It’s a classic failure of capitalism: when demand dramatically outstrips supply, quality decreases without consequences to the supplier. This goes for jobs, treatment by employers, and even job descriptions. They were never exactly the pinnacle of eloquence, but I’ve seen a serious decline in the past year or so.

Many legal filings written by attorneys are also full of reader-hostile jargon and nonsense clearly included because the lawyer’s writing professor said it should be included, or because the named partner at their first firm always used it. It’s one of the most infuriating and offensive aspects of modern U.S. professional culture as far I’m concerned:

“We do it this way because we do it this way, because the people before us did it this way, that’s why we do it this way.”

Never, ever say that to me. It triggers an almost instinctual, lizard-brain contempt in me and an assumption that whoever said it is incapable of critical thinking or analytical reasoning, and I can be a real asshole when I think that about someone.

#Links #Link #writing #human resources #job descriptions #machine learning #organizational development #professionalism #Shivon Zilis