The Way We Work
Priyanka Jain: How to make applying for jobs less painful
Prijanka Đajin (Priyanka Jain): Kako da konkurisanje za poslove postane manje bolno
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Pronalaženje posla je ranije počinjalo slanjem biografije na milion oglasa, i zatim nikada ne bismo dobili povratnu informaciju u većini slučajeva. Ali sve više kompanija koristi napredne metode za pronalaženje kandidata. Ako je veštačka inteligencija budućnost zapošljavanja, šta to znači za vas? Tehnolog Prijanka Đajin daje pregled ovog novog terena zapošljavanja.
Priyanka Jain - Technologist
Priyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective. Full bio
Priyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective. Full bio
Double-click the English transcript below to play the video.
Konkurisanje za poslove onlajn
00:00
Applying for jobs online
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00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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je jedno od najgorih digitalnih
iskustava našeg vremena.
iskustava našeg vremena.
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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A ni konkurisanje uživo
zapravo nije mnogo bolje.
zapravo nije mnogo bolje.
00:07
[The Way We Work]
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[Način na koji radimo]
Zapošljavanje kakvo poznajemo
je manjkavo na više načina.
je manjkavo na više načina.
00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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00:14
It's a terrible experience for people.
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To je grozno iskustvo za ljude.
Oko 75 posto ljudi
koji su se prijavljivali za poslove
koji su se prijavljivali za poslove
00:15
About 75 percent of people
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00:17
who applied to jobs
using various methods in the past year
using various methods in the past year
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pomoću različitih metoda prošle godine
reklo je da nikad nisu ništa
čuli od poslodavca.
čuli od poslodavca.
00:20
said they never heard anything back
from the employer.
from the employer.
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00:23
And at the company level
it's not much better.
it's not much better.
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Na nivou kompanije nije ništa bolje.
Četrdeset šest posto ljudi
dobije ili da otkaz
dobije ili da otkaz
00:25
46 percent of people get fired or quit
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00:28
within the first year
of starting their jobs.
of starting their jobs.
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u prvoj godini zaposlenja.
00:30
It's pretty mind-blowing.
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To je prilično zapanjujuće.
00:31
It's also bad for the economy.
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Takođe je loše za ekonomiju.
Prvi put u istoriji,
00:33
For the first time in history,
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00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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imamo više slobodnih radnih mesta
nego nezaposlenih ljudi,
nego nezaposlenih ljudi,
00:37
and to me that screams
that we have a problem.
that we have a problem.
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i to po meni jasno govori
da imamo problem.
da imamo problem.
Smatram da je u srži svega ovoga
jedan papir: biografija.
jedan papir: biografija.
00:39
I believe that at the crux of all of this
is a single piece of paper: the résumé.
is a single piece of paper: the résumé.
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Biografija definitivno sadrži
neke korisne informacije:
neke korisne informacije:
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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00:46
what roles people have had,
computer skills,
computer skills,
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koje poslove su ljudi obavljali,
veštine na računaru, koje jezike govore,
veštine na računaru, koje jezike govore,
00:48
what languages they speak,
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00:49
but what it misses is
what they have the potential to do
what they have the potential to do
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ali izostavlja njihov potencijal
da urade nešto
da urade nešto
00:52
that they might not have had
the opportunity to do in the past.
the opportunity to do in the past.
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što možda nisu imali priliku
da urade u prošlosti.
da urade u prošlosti.
Sa ekonomijom koja se tako brzo menja
i u kojoj se pojavljuju poslovi
i u kojoj se pojavljuju poslovi
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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00:58
that might require skills that nobody has,
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koji mogu zahtevati veštine
koje niko nema,
koje niko nema,
01:01
if we only look at what someone
has done in the past,
has done in the past,
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ako samo gledamo
šta je neko radio u prošlosti,
šta je neko radio u prošlosti,
01:03
we're not going to be able
to match people to the jobs of the future.
to match people to the jobs of the future.
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nećemo moći da povežemo ljude
sa poslovima budućnosti.
sa poslovima budućnosti.
Mislim da bi tu tehnologija
mogla bila od velike pomoći.
mogla bila od velike pomoći.
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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Verovatno ste videli
da su algoritmi postali prilično dobri
da su algoritmi postali prilično dobri
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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u povezivanju ljudi sa raznim stvarima,
01:12
at matching people to things,
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ali šta ako bismo mogli
da upotrebimo tu istu tehnologiju
da upotrebimo tu istu tehnologiju
01:14
but what if we could use
that same technology
that same technology
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01:16
to actually help us find jobs
that we're really well-suited for?
that we're really well-suited for?
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da nam pomogne da nađemo poslove
za koje smo zaista podesni?
za koje smo zaista podesni?
Znam šta mislite.
01:19
But I know what you're thinking.
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Zvuči pomalo zastrašujuće
da algoritmi biraju vaš sledeći posao,
da algoritmi biraju vaš sledeći posao,
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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ali postoji nešto što se pokazalo
01:24
but there is one thing that has been shown
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da dobro predviđa
budući uspeh osobe na poslu,
budući uspeh osobe na poslu,
01:26
to be really predictive
of someone's future success in a job,
of someone's future success in a job,
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a to nešto se zove
test višestrukih merila.
test višestrukih merila.
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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Testovi višestrukih merila
nisu ništa novo,
nisu ništa novo,
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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ali ranije su bili jako skupi
01:33
but they used to be really expensive
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i zahtevali su da doktor nauka
sedi naspram vas,
sedi naspram vas,
01:35
and required a PhD sitting across from you
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odgovaranje na mnogo pitanja
i pisanje izveštaja.
i pisanje izveštaja.
01:37
and answering lots of questions
and writing reports.
and writing reports.
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01:39
Multimeasure tests are a way
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Testovi višestrukih merila su način
01:41
to understand someone's inherent traits --
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za razumevanje
nečijih unutrašnjih osobina -
nečijih unutrašnjih osobina -
01:43
your memory, your attentiveness.
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vašeg pamćenja, pažljivosti.
01:46
What if we could take multimeasure tests
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Šta ako bismo testove višestrukih merila
01:48
and make them scalable and accessible,
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učinili prilagodljivim i pristupačnim
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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i obezbedili podatke poslodavcima
o tome koje su to karakteristike
o tome koje su to karakteristike
01:54
of someone who can make
them a good fit for a job?
them a good fit for a job?
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osobe koja bi odgovarala nekom poslu?
01:57
This all sounds abstract.
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Sve ovo zvuči apstraktno.
01:58
Let's try one of the games together.
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Hajde da probamo jednu igru zajedno.
Videćete krug koji treperi
02:00
You're about to see a flashing circle,
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i vaš zadatak će biti da udarite dlanom
o dlan kada je krug crven,
o dlan kada je krug crven,
02:02
and your job is going to be
to clap when the circle is red
to clap when the circle is red
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a ne uradite ništa kada je zelen.
02:06
and do nothing when it's green.
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02:07
[Ready?]
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[Spremni?]
02:09
[Begin!]
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[Počnite!]
02:11
[Green circle]
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[Zeleni krug]
02:13
[Green circle]
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[Zeleni krug]
02:15
[Red circle]
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[Crveni krug]
02:17
[Green circle]
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[Zeleni krug]
02:19
[Red circle]
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[Crveni krug]
02:21
Maybe you're the type of person
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Možda ste tip osobe
koja pljesne milisekundu
nakon što se crveni krug pojavi.
nakon što se crveni krug pojavi.
02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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02:26
Or maybe you're the type of person
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Ili ste možda tip osobe
koja čeka malo više
da bi bila 100 posto sigurna.
da bi bila 100 posto sigurna.
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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Ili možda tapšete na zeleno
iako ne bi trebalo.
iako ne bi trebalo.
02:30
Or maybe you clap on green
even though you're not supposed to.
even though you're not supposed to.
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Ovde je zanimljivo
da to nije kao standardizovan test
da to nije kao standardizovan test
02:33
The cool thing here is that
this isn't like a standardized test
this isn't like a standardized test
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gde su neki ljudi podobni
za zaposlenje, a neki nisu.
za zaposlenje, a neki nisu.
02:36
where some people are employable
and some people aren't.
and some people aren't.
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Umesto toga, radi se o razumevanju
uklapanja vaših karakteristika
uklapanja vaših karakteristika
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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i onog zbog čega biste bili dobri
na izvesnom poslu.
na izvesnom poslu.
02:42
and what would make you
good a certain job.
good a certain job.
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Otkrili smo da, ako pljesnete kasno
na crveno i ne pljesnete na zeleno,
na crveno i ne pljesnete na zeleno,
02:44
We found that if you clap late on red
and you never clap on the green,
and you never clap on the green,
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02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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možete biti veoma pažljivi i uzdržani.
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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Ljudi u tom kvadrantu su često
dobri učenici, dobro prolaze na testovima,
dobri učenici, dobro prolaze na testovima,
odlični u upravljanju projektima
ili u računovodstvu.
ili u računovodstvu.
02:55
great at project management or accounting.
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02:57
But if you clap immediately on red
and sometimes clap on green,
and sometimes clap on green,
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Ali ako odmah pljesnete na crveno
i ponekad na zeleno,
i ponekad na zeleno,
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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to može značiti da ste više
impulsivni i kreativni,
impulsivni i kreativni,
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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i otkrili smo da vrhunski prodavci
često poseduju ove osobine.
često poseduju ove osobine.
03:07
The way we actually use this in hiring
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Ovo koristimo u zapošljavanju
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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tako što ljude koji su se istakli na poslu
podvrgnemo neuronaučnim vežbama
podvrgnemo neuronaučnim vežbama
03:12
like this one.
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kao što je ova.
03:14
Then we develop an algorithm
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Zatim razvijemo algoritam
03:15
that understands what makes
those top performers unique.
those top performers unique.
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koji razume šta te odlične radnike
čini jedinstvenim.
čini jedinstvenim.
A onda, kada ljudi konkurišu za posao,
03:18
And then when people apply to the job,
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03:20
we're able to surface the candidates
who might be best suited for that job.
who might be best suited for that job.
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možemo da iznedrimo kandidate
koji bi mogli biti
najpogodniji za taj posao.
najpogodniji za taj posao.
Možda mislite da se u ovome
krije opasnost.
krije opasnost.
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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Svet rada danas nije baš najraznovrsniji
03:26
The work world today
is not the most diverse
is not the most diverse
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i ako ćemo stvarati algoritme
na osnovu trenutno najboljih radnika,
na osnovu trenutno najboljih radnika,
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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kako da budemo sigurni
03:32
how do we make sure
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da time nećemo samo održavati
predrasude koje već postoje?
predrasude koje već postoje?
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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Na primer, ako pravimo algoritam
zasnovan na vrhunskim direktorima
zasnovan na vrhunskim direktorima
03:36
For example, if we were building
an algorithm based on top performing CEOs
an algorithm based on top performing CEOs
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03:40
and use the S&P 500 as a training set,
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i koristimo indeks S&P 500
kao početni skup podataka,
kao početni skup podataka,
03:43
you would actually find
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otkrili bismo
03:44
that you're more likely to hire
a white man named John than any woman.
a white man named John than any woman.
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da je verovatnije da ćete zaposliti
belca po imenu Džon nego neku ženu.
belca po imenu Džon nego neku ženu.
To je stvarna slika toga
ko se sada nalazi na tim poslovima.
ko se sada nalazi na tim poslovima.
03:48
And that's the reality
of who's in those roles right now.
of who's in those roles right now.
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03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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Ali tehnologija zapravo predstavlja
zaista zanimljivu priliku.
zaista zanimljivu priliku.
03:54
We can create algorithms
that are more equitable
that are more equitable
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Možemo napraviti algoritme
koji su pravedniji
koji su pravedniji
03:57
and more fair than human beings
have ever been.
have ever been.
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i pošteniji od ljudskih bića.
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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Svaki algoritam koji smo stavili
u upotrebu je prethodno testiran
u upotrebu je prethodno testiran
da bi se osiguralo da ne favorizuje
jedan pol ili etničku pripadnost.
jedan pol ili etničku pripadnost.
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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A ako postoji neka populacija
koja je više favorizovana,
koja je više favorizovana,
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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možemo da izmenimo algoritam
da više ne bude tako.
da više ne bude tako.
04:12
When we focus on the inherent
characteristics
characteristics
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Kada se fokusiramo na unutrašnje osobine
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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zbog kojih je neko
dobra osoba za neki posao,
dobra osoba za neki posao,
možemo prevazići rasizam, klasizam,
seksizam, starosnu diskriminaciju -
seksizam, starosnu diskriminaciju -
04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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čak i obrazovnu diskriminaciju.
04:20
even good schoolism.
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Naša najbolja tehnologija i algoritmi
ne bi trebalo da se samo koriste
ne bi trebalo da se samo koriste
04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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04:24
for helping us find our next movie binge
or new favorite Justin Bieber song.
or new favorite Justin Bieber song.
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za pomoć u pronalaženju narednog filma
ili nove omiljene pesme Džastina Bibera.
ili nove omiljene pesme Džastina Bibera.
04:28
Imagine if we could harness
the power of technology
the power of technology
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Zamislite ako bismo iskoristili
moć tehnologije
moć tehnologije
da dobijemo stvarne smernice
za ono što treba da radimo
za ono što treba da radimo
04:31
to get real guidance
on what we should be doing
on what we should be doing
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04:33
based on who we are at a deeper level.
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na osnovu onoga što jesmo
na dubljem nivou.
na dubljem nivou.
ABOUT THE SPEAKER
Priyanka Jain - TechnologistPriyanka Jain heads up product for pymetrics, an NYC-based startup that uses neuroscience and AI to make hiring more diverse and effective.
Why you should listen
Passionate about using technology to create a fairer workplace and global economy, Priyanka Jain is a spokesperson for the United Nations Foundation's Girl Up Campaign, Chair of the Acumen Fund's Junior Council and on the Innovation Board for the XPrize Foundation. She received her B.S. from Stanford University, where she was President of Stanford Women in Business and one of 12 Mayfield Entrepreneurship Fellows. Her previous experience includes internships at IBM Watson, Shift Technologies, Canvas Ventures and the Institute for Learning and Brain Sciences. Outside of work, she loves playing tennis and eating anything covered in dark chocolate.
Priyanka Jain | Speaker | TED.com