Jobsøgning på nettet er en af de værste digitale oplevelser i vor tid. Og personlig jobsøgning er ikke meget bedre. [Måden vi arbejder på]
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. [The Way We Work]
Ansættelse som vi kender det, fungerer ikke Det er en dårlig oplevelse. Omtrent 75% af de der søgte job det seneste år fortæller at de aldrig hørte noget fra arbejdsgiveren. I virksomhederne er det ikke meget bedre 46% af de ansatte bliver fyret eller siger op indenfor det første år af deres ansættelse. Det er chokerende tal. Og skidt for økonomien. For første gang i historien har vi flere ledige jobs end arbejdsløse i USA, og det viser et tydeligt problem
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.
Jeg tror sagens kerne er et enkelt stykke papir: CV'et. Et CV indeholder utvivlsomt vigtig information: tidligere ansættelser, IT-evner, hvilke sprog de taler, men sjældent noget om de potentialer der måske ikke tidligere er blevet udlevet Og med en økonomi i forandring, hvor de job der opslås på nettet måske kræver evner som ingen har ...
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,
hvis vi kun kigger på hvad folk har lavet tidligere, vil vi ikke kunne matche folk til de rette jobs i fremtiden.
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.
Det er her teknologien kan være til stor hjælp Algoritmer er gode til at matche folk med ting, men måske kan vi anvende denne teknologi til at finde jobs vi matcher godt? Men jeg ved hvad I tænker. Algoritmer der vælger dit næste job, lyder lidt skræmmende, men der er en faktor som påviseligt er en stærk indikator for en kandidats fremtidige jobsucces og det er en såkaldt multifaktortest.
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.
Multifaktortests er egentlig ikke nye, men de plejede at være meget dyre og krævede at en PhD sad sammen med dig og svar på en masse spørgsmål og rapportskrivning. Multifaktortests er en måde at afdække folks naturlige talenter - deres hukommelse, deres opmærksomhed. Hvad hvis vi kunne gøre multifaktortests tilgængelige i stor skala og levere data til arbejdsgivere om hvilke egenskaber den bedste ansøger til jobbet bør have.
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?
Det kan lyde abstrakt. Lad os prøve en af testene sammen. Snart vil I se en blinkende cirkel, og jeres opgave er at klappe, når cirklen er rød og ingenting, når den er grøn.
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.
[Klar?]
[Ready?]
[Start!]
[Begin!]
[Grøn cirkel]
[Green circle]
[Grøn cirkel]
[Green circle]
[Rød cirkel]
[Red circle]
[Grøn cirkel]
[Green circle]
[Rød cirkel]
[Red circle]
Måske er du typen der klapper øjeblikkeligt, når en rød cirkel vises. Eller måske er du typen, der lige tager sig tid til at være 100% sikker. Eller måske klapper du på grøn, selvom det ikke er meningen. Det fede her er, at det ikke er som en standardiseret test, hvor nogle kan bruges og andre ikke kan. I stedet handler det om at forstå matchet mellem dine talenter og hvad der gør dig egnet til et givent job Vi har opdaget, at hvis du klapper sent på rød og aldrig på grøn, så scorer du nok højt i opmærksomhed og højt i tilbageholdenhed. Folk i den kvadrant er ofte gode til at studere og tage tests, gode til projektledelse eller bogføring. Men hvis du klapper øjeblikkeligt på rød og nogle gange på grøn, kan det betyde, at du er mere impulsiv og kreativ Og vi ser at de bedste sælgere ofte besidder disse talenter.
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.
Vi bruger dette i ansættelser ved at lade de dygtigste udføre neurovidenskabelige øvelser som denne. Så udvikler vi en algoritme, der kan udpege, hvad der gør dem i toppen unikke. Og når folk så søger jobbet, kan vi udpege de kandidater, der sandsynligvis er bedst.
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.
Du fornemmer måske en risiko Arbejdsmarkedet i dag er ikke så alsidigt, og hvis vi laver algoritmer baseret på de dygtigste i job, hvordan sikrer vi så at vi ikke gror fast i gamle mønstre? F.eks. hvis algoritmen baseres på de dygtigste topchefer og fodres med data fra S&P 500 (de største amerikanske virksomheder) ville du opdage at flere hvide mænd hedder John end der totalt set er kvinder. Sådan er sammensætningen af dem, der har disse jobs nu. Men teknologien giver os en interessant mulighed. Vi kan lave algoritmer, der er mere retfærdige og mere fair end mennesker nogensinde har været. Alle vores algoritmer er testet for favorisering af køn og etnicitet Og hvis en bestemt gruppe favoriseres, så kan vi ændre algoritmen så dette ikke sker. Når vi fokuserer på de naturlige talenter, der er basis for et et godt match, kan vi undgå diskrimination på race, køn, klasse og alder - sågar uddannelsesniveau.
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.
Vores bedste teknologi og algoritmer skal ikke bare bruges til at finde den næste film eller favoritsang med Justin Bieber. Vi kan udnytte teknologiens kraft til at få reel vejledning i hvad vi burde lave baseret på hvem vi er inderst inde.
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.