The Way We Work
Priyanka Jain: How to make applying for jobs less painful
Priyanka Jain: Como facer que a solicitude de emprego sexa menos dolorosa
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Atopar un traballo adoitaba comezar coa presentación do teu currículum a un millón de anuncios e nunca volver saber da maioría deles. Pero cada vez son máis as empresas que usan métodos de tecnoloxía avanzada para identificar os candidatos. Se a IA é o futuro da contratación, que significa isto para ti? A tecnóloga Priyanka Jain dá unha ollada a esta nova paisaxe de contratación.
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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Solicitar emprego en liña
00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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é unha das peores
experiencias dixitais do noso tempo.
experiencias dixitais do noso tempo.
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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E solicitalo de xeito presencial
non é moito mellor.
non é moito mellor.
00:07
[The Way We Work]
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[Cómo traballamos]
00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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A contratación actual
está rota en moitas frontes.
está rota en moitas frontes.
É unha experiencia terrible para a xente.
00:14
It's a terrible experience for people.
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Arredor do 75 por cento das persoas
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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que solicitaron un traballo
por varios métodos no último ano
por varios métodos no último ano
00:20
said they never heard anything back
from the employer.
from the employer.
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dixeron que o empresario
nunca lles contestou.
nunca lles contestou.
00:23
And at the company level
it's not much better.
it's not much better.
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E a nivel de empresa non é moito mellor.
00:25
46 percent of people get fired or quit
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O 46 % das persoas
son despedidas ou deixan o traballo
son despedidas ou deixan o traballo
00:28
within the first year
of starting their jobs.
of starting their jobs.
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ao primeiro ano de comezalo.
00:30
It's pretty mind-blowing.
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É terrorífico.
00:31
It's also bad for the economy.
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E tamén é malo para a economía.
00:33
For the first time in history,
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Por primeira vez na historia,
00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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temos máis emprego dispoñible
que persoas desempregadas,
que persoas desempregadas,
00:37
and to me that screams
that we have a problem.
that we have a problem.
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e para min iso indica
que temos un problema.
que temos un problema.
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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Creo que na base de todo isto
hai un único papel: o currículo vitae.
hai un único papel: o currículo vitae.
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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Un currículo ten algunhas partes útiles:
00:46
what roles people have had,
computer skills,
computer skills,
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os roles da xente,
as habilidades informáticas,
as habilidades informáticas,
00:48
what languages they speak,
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os idiomas que falan,
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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pero o que falta é
dicir qué potencial teñen de facer
dicir qué potencial teñen de facer
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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o que quizais non tivesen
a oportunidade de facer no pasado.
a oportunidade de facer no pasado.
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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E cunha economía tan cambiante
onde os traballos que veñen en liña
onde os traballos que veñen en liña
00:58
that might require skills that nobody has,
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poden requirir
habilidades que ninguén ten,
habilidades que ninguén ten,
01:01
if we only look at what someone
has done in the past,
has done in the past,
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se só miramos o que alguén
fixo no pasado,
fixo no pasado,
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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non imos poder vincular a xente
aos traballos do futuro.
aos traballos do futuro.
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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Aquí é onde creo que a tecnoloxía
pode ser de gran axuda.
pode ser de gran axuda.
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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Probablemente xa viron
que os algoritmos
que os algoritmos
son bos relacionando
a xente con cousas,
a xente con cousas,
01:12
at matching people to things,
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01:14
but what if we could use
that same technology
that same technology
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pero, e se puidésemos usar
esa mesma tecnoloxía
esa mesma tecnoloxía
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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para realmente axudarnos a atopar
un emprego que se adapte a nós?
un emprego que se adapte a nós?
01:19
But I know what you're thinking.
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Xa sei o que está a pensar.
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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Dá medo que os algoritmos
escollan o seu seguinte traballo,
escollan o seu seguinte traballo,
01:24
but there is one thing that has been shown
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pero hai unha cousa que demostrou
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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ser realmente premonitoria
no éxito futuro de alguén nun traballo,
no éxito futuro de alguén nun traballo,
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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e chámase proba multimedida.
As probas multimedida
non son realmente nada novo,
non son realmente nada novo,
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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01:33
but they used to be really expensive
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pero adoitaban ser moi caras
e precisaban dun doutorando
sentado ao seu carón
sentado ao seu carón
01:35
and required a PhD sitting across from you
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01:37
and answering lots of questions
and writing reports.
and writing reports.
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respondendo moitas preguntas
e escribindo informes.
e escribindo informes.
01:39
Multimeasure tests are a way
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As probas multimedida son un xeito
de comprender
os rasgos inherentes de alguén,
os rasgos inherentes de alguén,
01:41
to understand someone's inherent traits --
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01:43
your memory, your attentiveness.
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a súa memoria, a súa atención.
01:46
What if we could take multimeasure tests
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E se puidésemos facer probas multimedida
01:48
and make them scalable and accessible,
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escalables e accesibles,
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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e proporcionar datos aos empresarios
sobre cales son os rasgos
sobre cales son os rasgos
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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de alguén que poida encaixar nun traballo?
01:57
This all sounds abstract.
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Todo isto soa abstracto.
01:58
Let's try one of the games together.
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Probemos un dos xogos xuntos.
02:00
You're about to see a flashing circle,
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Agora verá un círculo intermitente,
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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e o seu traballo vai ser
aplaudir cando o círculo está vermello
aplaudir cando o círculo está vermello
02:06
and do nothing when it's green.
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e non facelo cando estea verde.
02:07
[Ready?]
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[Listo?]
02:09
[Begin!]
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[Comeza!]
02:11
[Green circle]
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[Círculo verde]
02:13
[Green circle]
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[Círculo verde]
02:15
[Red circle]
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[Círculo vermello]
02:17
[Green circle]
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[Círculo verde]
02:19
[Red circle]
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[Círculo vermello]
02:21
Maybe you're the type of person
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Quizais sexa o tipo de persoa
02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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que aplaude un milisegundo despois
de ver o círculo vermello.
de ver o círculo vermello.
02:26
Or maybe you're the type of person
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Ou quizais sexa do tipo
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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que lle leva un pouco máis
estar 100 por cento seguro.
estar 100 por cento seguro.
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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Ou quizais aplaude ao verde
aínda que se supón que non debe.
aínda que se supón que non debe.
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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O xenial aquí é que isto non é
como unha proba normalizada
como unha proba normalizada
02:36
where some people are employable
and some people aren't.
and some people aren't.
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onde algunhas persoas
son empregables e outras non.
son empregables e outras non.
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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Trátase de comprender
o axuste entre as súas características
o axuste entre as súas características
02:42
and what would make you
good a certain job.
good a certain job.
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que o farían bo para determinado traballo.
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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Descubrimos que se aplaude ao vermello
e nunca aplaude ao verde,
e nunca aplaude ao verde,
02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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pode ter un alto grado de atención
e de contención.
e de contención.
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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As persoas así adoitan ser
grandes estudantes e probadores,
grandes estudantes e probadores,
e bos na xestión de proxectos
ou en contabilidade.
ou en contabilidade.
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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Pero se aplaude inmediatamente
ao vermello e ás veces ao verde,
ao vermello e ás veces ao verde,
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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podería significar que é vostede
máis impulsivo e creativo,
máis impulsivo e creativo,
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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e descubrimos que estes son os rasgos
dos vendedores de mellor rendemento.
dos vendedores de mellor rendemento.
03:07
The way we actually use this in hiring
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O xeito no que usamos isto na contratación
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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é facendo exercicios de neurociencia
como este cos mellores.
como este cos mellores.
03:12
like this one.
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03:14
Then we develop an algorithm
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coma este.
Logo creamos un algoritmo
03:15
that understands what makes
those top performers unique.
those top performers unique.
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que entende que é o que fai únicos
aos máis destacados.
aos máis destacados.
03:18
And then when people apply to the job,
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E cando a xente solicita o traballo,
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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podemos escoller ao máis adecuado
para ese traballo de entre os candidatos.
para ese traballo de entre os candidatos.
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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Pode estar a pensar
que hai un perigo nisto.
que hai un perigo nisto.
03:26
The work world today
is not the most diverse
is not the most diverse
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O mundo laboral de hoxe
non é o máis diverso
non é o máis diverso
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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e se facemos algoritmos
en base aos mellores na actualidade,
en base aos mellores na actualidade,
03:32
how do we make sure
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como nos aseguramos
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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de non estar a perpetuar
os sesgos xa existentes?
os sesgos xa existentes?
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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Por exemplo, se construimos un algoritmo
baseado nos CEOs de mellor rendemento
baseado nos CEOs de mellor rendemento
03:40
and use the S&P 500 as a training set,
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e usamos o S&P 500
como conxunto de adestramento,
como conxunto de adestramento,
03:43
you would actually find
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atopariamos que hai máis probabilidades
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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de contratar a un home branco
chamado Xoán que a calquera muller.
chamado Xoán que a calquera muller.
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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Esa é a realidade
de quen está nesas posicións agora.
de quen está nesas posicións agora.
03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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Pero a tecnoloxía plantexa
unha oportunidade realmente interesante.
unha oportunidade realmente interesante.
03:54
We can create algorithms
that are more equitable
that are more equitable
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Podemos crear algoritmos máis equitativos
03:57
and more fair than human beings
have ever been.
have ever been.
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e máis xustos do que
algunha vez foi o ser humano.
algunha vez foi o ser humano.
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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Todos os algoritmos que facemos
foron probados antes
foron probados antes
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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para asegurar que non favorece
a ningún xénero ou etnia.
a ningún xénero ou etnia.
04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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E se hai algunha poboación
que está sendo desfavorecida,
que está sendo desfavorecida,
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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podemos alterar o algoritmo
ata que iso non ocorra.
ata que iso non ocorra.
04:12
When we focus on the inherent
characteristics
characteristics
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Cando nos centramos
nas características inherentes
nas características inherentes
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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que fan a alguén ideal para un traballo,
04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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podemos transcender o racismo,
clasismo, sexismo, idade,
clasismo, sexismo, idade,
e incluso o bo escolarismo.
04:20
even good schoolism.
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04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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A nosa mellor tecnoloxía e algoritmos
non debería usarse
non debería usarse
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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para axudarnos a atopar a próxima película
ou o último éxito de Justin Bieber.
ou o último éxito de Justin Bieber.
04:28
Imagine if we could harness
the power of technology
the power of technology
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Imaxine se puidésemos aproveitar
o poder da tecnoloxía
o poder da tecnoloxía
04:31
to get real guidance
on what we should be doing
on what we should be doing
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para ter unha orientación real
do que deberiamos estar a facer
do que deberiamos estar a facer
04:33
based on who we are at a deeper level.
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en función do que somos
a un nivel máis profundo.
a un nivel máis profundo.
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