The Way We Work
Priyanka Jain: How to make applying for jobs less painful
Priyanka Jain: Sådan bliver jobsøgning mindre pinefuld
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Tidligere startede jobsøgning med at man sendte sit CV en million steder hen og aldrig hørte fra de fleste af dem. Nu bruger flere og flere virksomheder screeningsteknologi til at udvælge kandidater. Hvis kunstig intelligens er fremtiden indenfor jobsøgning, hvad betyder det så for dig? Teknologieksperten Priyanka Jain deler sin viden om jobsøgningens nye verden.
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.
00:00
Applying for jobs online
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Jobsøgning på nettet
00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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er en af de værste digitale
oplevelser i vor tid.
oplevelser i vor tid.
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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Og personlig jobsøgning
er ikke meget bedre.
er ikke meget bedre.
00:07
[The Way We Work]
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[Måden vi arbejder på]
00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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Ansættelse som vi
kender det, fungerer ikke
kender det, fungerer ikke
00:14
It's a terrible experience for people.
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Det er en dårlig oplevelse.
00:15
About 75 percent of people
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Omtrent 75% af de
00:17
who applied to jobs
using various methods in the past year
using various methods in the past year
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der søgte job
det seneste år
det seneste år
00:20
said they never heard anything back
from the employer.
from the employer.
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fortæller at de aldrig hørte
noget fra arbejdsgiveren.
noget fra arbejdsgiveren.
00:23
And at the company level
it's not much better.
it's not much better.
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I virksomhederne er
det ikke meget bedre
det ikke meget bedre
00:25
46 percent of people get fired or quit
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46% af de ansatte
bliver fyret eller siger op
bliver fyret eller siger op
00:28
within the first year
of starting their jobs.
of starting their jobs.
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indenfor det første år
af deres ansættelse.
af deres ansættelse.
00:30
It's pretty mind-blowing.
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Det er chokerende tal.
00:31
It's also bad for the economy.
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Og skidt for økonomien.
00:33
For the first time in history,
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For første gang i historien
00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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har vi flere ledige jobs
end arbejdsløse i USA,
end arbejdsløse i USA,
00:37
and to me that screams
that we have a problem.
that we have a problem.
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og det viser et
tydeligt problem
tydeligt problem
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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Jeg tror sagens kerne er et
enkelt stykke papir: CV'et.
enkelt stykke papir: CV'et.
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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Et CV indeholder utvivlsomt
vigtig information:
vigtig information:
00:46
what roles people have had,
computer skills,
computer skills,
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tidligere ansættelser,
IT-evner,
IT-evner,
00:48
what languages they speak,
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hvilke sprog de taler,
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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men sjældent noget om de potentialer
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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der måske ikke tidligere
er blevet udlevet
er blevet udlevet
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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Og med en økonomi i forandring,
hvor de job der opslås på nettet
hvor de job der opslås på nettet
00:58
that might require skills that nobody has,
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måske kræver evner som ingen har ...
01:01
if we only look at what someone
has done in the past,
has done in the past,
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hvis vi kun kigger på hvad
folk har lavet tidligere,
folk har lavet tidligere,
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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vil vi ikke kunne matche folk
til de rette jobs i fremtiden.
til de rette jobs i fremtiden.
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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Det er her teknologien
kan være til stor hjælp
kan være til stor hjælp
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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Algoritmer er gode til
01:12
at matching people to things,
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at matche folk med ting,
01:14
but what if we could use
that same technology
that same technology
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men måske kan vi anvende
denne teknologi
denne teknologi
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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til at finde jobs
vi matcher godt?
vi matcher godt?
01:19
But I know what you're thinking.
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Men jeg ved hvad I tænker.
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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Algoritmer der vælger dit næste job,
lyder lidt skræmmende,
lyder lidt skræmmende,
01:24
but there is one thing that has been shown
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men der er en faktor som påviseligt er
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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en stærk indikator for en
kandidats fremtidige jobsucces
kandidats fremtidige jobsucces
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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og det er en såkaldt multifaktortest.
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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Multifaktortests er egentlig ikke nye,
01:33
but they used to be really expensive
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men de plejede at være meget dyre
01:35
and required a PhD sitting across from you
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og krævede at en PhD sad sammen med dig
01:37
and answering lots of questions
and writing reports.
and writing reports.
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og svar på en masse spørgsmål
og rapportskrivning.
og rapportskrivning.
01:39
Multimeasure tests are a way
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Multifaktortests er en måde
01:41
to understand someone's inherent traits --
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at afdække folks naturlige talenter -
01:43
your memory, your attentiveness.
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deres hukommelse, deres opmærksomhed.
01:46
What if we could take multimeasure tests
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Hvad hvis vi kunne gøre multifaktortests
01:48
and make them scalable and accessible,
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tilgængelige i stor skala
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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og levere data til arbejdsgivere om
hvilke egenskaber den bedste ansøger
hvilke egenskaber den bedste ansøger
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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til jobbet bør have.
01:57
This all sounds abstract.
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Det kan lyde abstrakt.
01:58
Let's try one of the games together.
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Lad os prøve en af testene sammen.
02:00
You're about to see a flashing circle,
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Snart vil I se en blinkende cirkel,
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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og jeres opgave er at klappe,
når cirklen er rød
når cirklen er rød
02:06
and do nothing when it's green.
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og ingenting, når den er grøn.
02:07
[Ready?]
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[Klar?]
02:09
[Begin!]
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[Start!]
02:11
[Green circle]
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[Grøn cirkel]
02:13
[Green circle]
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[Grøn cirkel]
02:15
[Red circle]
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[Rød cirkel]
02:17
[Green circle]
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[Grøn cirkel]
02:19
[Red circle]
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[Rød cirkel]
02:21
Maybe you're the type of person
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Måske er du typen
02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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der klapper øjeblikkeligt,
når en rød cirkel vises.
når en rød cirkel vises.
02:26
Or maybe you're the type of person
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Eller måske er du typen,
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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der lige tager sig tid
til at være 100% sikker.
til at være 100% sikker.
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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Eller måske klapper du på grøn,
selvom det ikke er meningen.
selvom det ikke er meningen.
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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Det fede her er, at det ikke er
som en standardiseret test,
som en standardiseret test,
02:36
where some people are employable
and some people aren't.
and some people aren't.
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hvor nogle kan bruges
og andre ikke kan.
og andre ikke kan.
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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I stedet handler det om at forstå
matchet mellem dine talenter
matchet mellem dine talenter
02:42
and what would make you
good a certain job.
good a certain job.
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og hvad der gør dig egnet
til et givent job
til et givent job
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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Vi har opdaget, at hvis du klapper
sent på rød og aldrig på grøn,
sent på rød og aldrig på grøn,
02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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så scorer du nok højt i opmærksomhed
og højt i tilbageholdenhed.
og højt i tilbageholdenhed.
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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Folk i den kvadrant er ofte gode
til at studere og tage tests,
til at studere og tage tests,
02:55
great at project management or accounting.
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gode til projektledelse eller bogføring.
02:57
But if you clap immediately on red
and sometimes clap on green,
and sometimes clap on green,
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Men hvis du klapper øjeblikkeligt på rød
og nogle gange på grøn,
og nogle gange på grøn,
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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kan det betyde, at du er
mere impulsiv og kreativ
mere impulsiv og kreativ
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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Og vi ser at de bedste sælgere
ofte besidder disse talenter.
ofte besidder disse talenter.
03:07
The way we actually use this in hiring
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Vi bruger dette i ansættelser
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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ved at lade de dygtigste udføre
neurovidenskabelige øvelser
neurovidenskabelige øvelser
03:12
like this one.
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som denne.
03:14
Then we develop an algorithm
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Så udvikler vi en algoritme,
03:15
that understands what makes
those top performers unique.
those top performers unique.
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der kan udpege, hvad der
gør dem i toppen unikke.
gør dem i toppen unikke.
03:18
And then when people apply to the job,
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Og når folk så søger jobbet,
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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kan vi udpege de kandidater,
der sandsynligvis er bedst.
der sandsynligvis er bedst.
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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Du fornemmer
måske en risiko
måske en risiko
03:26
The work world today
is not the most diverse
is not the most diverse
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Arbejdsmarkedet i dag
er ikke så alsidigt,
er ikke så alsidigt,
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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og hvis vi laver algoritmer
baseret på de dygtigste i job,
baseret på de dygtigste i job,
03:32
how do we make sure
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hvordan sikrer vi så
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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at vi ikke gror fast
i gamle mønstre?
i gamle mønstre?
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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F.eks. hvis algoritmen baseres
på de dygtigste topchefer
på de dygtigste topchefer
03:40
and use the S&P 500 as a training set,
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og fodres med data fra S&P 500
(de største amerikanske virksomheder)
(de største amerikanske virksomheder)
03:43
you would actually find
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ville du opdage at
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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flere hvide mænd hedder John
end der totalt set er kvinder.
end der totalt set er kvinder.
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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Sådan er sammensætningen af dem,
der har disse jobs nu.
der har disse jobs nu.
03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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Men teknologien giver os
en interessant mulighed.
en interessant mulighed.
03:54
We can create algorithms
that are more equitable
that are more equitable
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Vi kan lave algoritmer,
der er mere retfærdige
der er mere retfærdige
03:57
and more fair than human beings
have ever been.
have ever been.
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og mere fair end mennesker
nogensinde har været.
nogensinde har været.
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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Alle vores algoritmer er testet
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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for favorisering af
køn og etnicitet
køn og etnicitet
04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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Og hvis en bestemt
gruppe favoriseres,
gruppe favoriseres,
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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så kan vi ændre algoritmen
så dette ikke sker.
så dette ikke sker.
04:12
When we focus on the inherent
characteristics
characteristics
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Når vi fokuserer på
de naturlige talenter,
de naturlige talenter,
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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der er basis for et
et godt match,
et godt match,
04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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kan vi undgå diskrimination på
race, køn, klasse og alder -
race, køn, klasse og alder -
04:20
even good schoolism.
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sågar uddannelsesniveau.
04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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Vores bedste teknologi og algoritmer
skal ikke bare bruges
skal ikke bare bruges
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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til at finde den næste film
eller favoritsang med Justin Bieber.
eller favoritsang med Justin Bieber.
04:28
Imagine if we could harness
the power of technology
the power of technology
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Vi kan udnytte
teknologiens kraft
teknologiens kraft
04:31
to get real guidance
on what we should be doing
on what we should be doing
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til at få reel vejledning i
hvad vi burde lave
hvad vi burde lave
04:33
based on who we are at a deeper level.
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baseret på hvem vi er inderst inde.
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