Applying for jobs online is one of the worst digital experiences of our time. And applying for jobs in person really isn't much better.
Konkurisanje za poslove onlajn je jedno od najgorih digitalnih iskustava našeg vremena. A ni konkurisanje uživo zapravo nije mnogo bolje.
[The Way We Work]
[Način na koji radimo]
Hiring as we know it is broken on many fronts. It's a terrible experience for people. About 75 percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer. And at the company level it's not much better. 46 percent of people get fired or quit within the first year of starting their jobs. It's pretty mind-blowing. It's also bad for the economy. For the first time in history, we have more open jobs than we have unemployed people, and to me that screams that we have a problem.
Zapošljavanje kakvo poznajemo je manjkavo na više načina. To je grozno iskustvo za ljude. Oko 75 posto ljudi koji su se prijavljivali za poslove pomoću različitih metoda prošle godine reklo je da nikad nisu ništa čuli od poslodavca. Na nivou kompanije nije ništa bolje. Četrdeset šest posto ljudi dobije ili da otkaz u prvoj godini zaposlenja. To je prilično zapanjujuće. Takođe je loše za ekonomiju. Prvi put u istoriji, imamo više slobodnih radnih mesta nego nezaposlenih ljudi, i to po meni jasno govori da imamo problem.
I believe that at the crux of all of this is a single piece of paper: the résumé. A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak, but what it misses is what they have the potential to do that they might not have had the opportunity to do in the past. And with such a quickly changing economy where jobs are coming online that might require skills that nobody has, if we only look at what someone has done in the past, we're not going to be able to match people to the jobs of the future.
Smatram da je u srži svega ovoga jedan papir: biografija. Biografija definitivno sadrži neke korisne informacije: koje poslove su ljudi obavljali, veštine na računaru, koje jezike govore, ali izostavlja njihov potencijal da urade nešto što možda nisu imali priliku da urade u prošlosti. Sa ekonomijom koja se tako brzo menja i u kojoj se pojavljuju poslovi koji mogu zahtevati veštine koje niko nema, ako samo gledamo šta je neko radio u prošlosti, nećemo moći da povežemo ljude sa poslovima budućnosti.
So this is where I think technology can be really helpful. You've probably seen that algorithms have gotten pretty good at matching people to things, but what if we could use that same technology to actually help us find jobs that we're really well-suited for? But I know what you're thinking. Algorithms picking your next job sounds a little bit scary, but there is one thing that has been shown to be really predictive of someone's future success in a job, and that's what's called a multimeasure test.
Mislim da bi tu tehnologija mogla bila od velike pomoći. Verovatno ste videli da su algoritmi postali prilično dobri u povezivanju ljudi sa raznim stvarima, ali šta ako bismo mogli da upotrebimo tu istu tehnologiju da nam pomogne da nađemo poslove za koje smo zaista podesni? Znam šta mislite. Zvuči pomalo zastrašujuće da algoritmi biraju vaš sledeći posao, ali postoji nešto što se pokazalo da dobro predviđa budući uspeh osobe na poslu, a to nešto se zove test višestrukih merila.
Multimeasure tests really aren't anything new, but they used to be really expensive and required a PhD sitting across from you and answering lots of questions and writing reports. Multimeasure tests are a way to understand someone's inherent traits -- your memory, your attentiveness. What if we could take multimeasure tests and make them scalable and accessible, and provide data to employers about really what the traits are of someone who can make them a good fit for a job?
Testovi višestrukih merila nisu ništa novo, ali ranije su bili jako skupi i zahtevali su da doktor nauka sedi naspram vas, odgovaranje na mnogo pitanja i pisanje izveštaja. Testovi višestrukih merila su način za razumevanje nečijih unutrašnjih osobina - vašeg pamćenja, pažljivosti. Šta ako bismo testove višestrukih merila učinili prilagodljivim i pristupačnim i obezbedili podatke poslodavcima o tome koje su to karakteristike osobe koja bi odgovarala nekom poslu?
This all sounds abstract. Let's try one of the games together. You're about to see a flashing circle, and your job is going to be to clap when the circle is red and do nothing when it's green.
Sve ovo zvuči apstraktno. Hajde da probamo jednu igru zajedno. Videćete krug koji treperi i vaš zadatak će biti da udarite dlanom o dlan kada je krug crven, a ne uradite ništa kada je zelen.
[Ready?]
[Spremni?]
[Begin!]
[Počnite!]
[Green circle]
[Zeleni krug]
[Green circle]
[Zeleni krug]
[Red circle]
[Crveni krug]
[Green circle]
[Zeleni krug]
[Red circle]
[Crveni krug]
Maybe you're the type of person who claps the millisecond after a red circle appears. Or maybe you're the type of person who takes just a little bit longer to be 100 percent sure. Or maybe you clap on green even though you're not supposed to. The cool thing here is that this isn't like a standardized test where some people are employable and some people aren't. Instead it's about understanding the fit between your characteristics and what would make you good a certain job. We found that if you clap late on red and you never clap on the green, you might be high in attentiveness and high in restraint. People in that quadrant tend to be great students, great test-takers, great at project management or accounting. But if you clap immediately on red and sometimes clap on green, that might mean that you're more impulsive and creative, and we've found that top-performing salespeople often embody these traits.
Možda ste tip osobe koja pljesne milisekundu nakon što se crveni krug pojavi. Ili ste možda tip osobe koja čeka malo više da bi bila 100 posto sigurna. Ili možda tapšete na zeleno iako ne bi trebalo. Ovde je zanimljivo da to nije kao standardizovan test gde su neki ljudi podobni za zaposlenje, a neki nisu. Umesto toga, radi se o razumevanju uklapanja vaših karakteristika i onog zbog čega biste bili dobri na izvesnom poslu. Otkrili smo da, ako pljesnete kasno na crveno i ne pljesnete na zeleno, možete biti veoma pažljivi i uzdržani. Ljudi u tom kvadrantu su često dobri učenici, dobro prolaze na testovima, odlični u upravljanju projektima ili u računovodstvu. Ali ako odmah pljesnete na crveno i ponekad na zeleno, to može značiti da ste više impulsivni i kreativni, i otkrili smo da vrhunski prodavci često poseduju ove osobine.
The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one. Then we develop an algorithm that understands what makes those top performers unique. And then when people apply to the job, we're able to surface the candidates who might be best suited for that job.
Ovo koristimo u zapošljavanju tako što ljude koji su se istakli na poslu podvrgnemo neuronaučnim vežbama kao što je ova. Zatim razvijemo algoritam koji razume šta te odlične radnike čini jedinstvenim. A onda, kada ljudi konkurišu za posao, možemo da iznedrimo kandidate koji bi mogli biti najpogodniji za taj posao.
So you might be thinking there's a danger in this. The work world today is not the most diverse and if we're building algorithms based on current top performers, how do we make sure that we're not just perpetuating the biases that already exist? For example, if we were building an algorithm based on top performing CEOs and use the S&P 500 as a training set, you would actually find that you're more likely to hire a white man named John than any woman. And that's the reality of who's in those roles right now. But technology actually poses a really interesting opportunity. We can create algorithms that are more equitable and more fair than human beings have ever been. Every algorithm that we put into production has been pretested to ensure that it doesn't favor any gender or ethnicity. And if there's any population that's being overfavored, we can actually alter the algorithm until that's no longer true. When we focus on the inherent characteristics that can make somebody a good fit for a job, we can transcend racism, classism, sexism, ageism -- even good schoolism.
Možda mislite da se u ovome krije opasnost. Svet rada danas nije baš najraznovrsniji i ako ćemo stvarati algoritme na osnovu trenutno najboljih radnika, kako da budemo sigurni da time nećemo samo održavati predrasude koje već postoje? Na primer, ako pravimo algoritam zasnovan na vrhunskim direktorima i koristimo indeks S&P 500 kao početni skup podataka, otkrili bismo da je verovatnije da ćete zaposliti belca po imenu Džon nego neku ženu. To je stvarna slika toga ko se sada nalazi na tim poslovima. Ali tehnologija zapravo predstavlja zaista zanimljivu priliku. Možemo napraviti algoritme koji su pravedniji i pošteniji od ljudskih bića. Svaki algoritam koji smo stavili u upotrebu je prethodno testiran da bi se osiguralo da ne favorizuje jedan pol ili etničku pripadnost. A ako postoji neka populacija koja je više favorizovana, možemo da izmenimo algoritam da više ne bude tako. Kada se fokusiramo na unutrašnje osobine zbog kojih je neko dobra osoba za neki posao, možemo prevazići rasizam, klasizam, seksizam, starosnu diskriminaciju - čak i obrazovnu diskriminaciju.
Our best technology and algorithms shouldn't just be used for helping us find our next movie binge or new favorite Justin Bieber song. Imagine if we could harness the power of technology to get real guidance on what we should be doing based on who we are at a deeper level.
Naša najbolja tehnologija i algoritmi ne bi trebalo da se samo koriste za pomoć u pronalaženju narednog filma ili nove omiljene pesme Džastina Bibera. Zamislite ako bismo iskoristili moć tehnologije da dobijemo stvarne smernice za ono što treba da radimo na osnovu onoga što jesmo na dubljem nivou.