The Way We Work
Priyanka Jain: How to make applying for jobs less painful
Priyanka Jain: Bagaimana untuk memudahkan proses memohon kerja
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Mencari pekerjaan selalunya dimulakan dengan menghantar resume ke seberapa banyak syarikat dan selalunya tidak mendapat apa-apa maklum balas dari mereka. Tetapi semakin banyak syarikat menggunakan kaedah 'tech-forward' untuk mengenalpasti calon. Jika Al adalah masa depan untuk pengambilan pekerja, apakah maksudnya untuk anda? Ahli teknologi Priyanka Jain memberi penerangan tentang lanskap baru proses pengambilan pekerja.
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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Memohon kerja secara online
00:01
is one of the worst
digital experiences of our time.
digital experiences of our time.
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adalah antara pengalaman pahit, permohonan
00:04
And applying for jobs in person
really isn't much better.
really isn't much better.
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kerja secara berhadapan juga
taklah begitu bagus.
taklah begitu bagus.
00:07
[The Way We Work]
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[Cara Kita Bekerja]
00:11
Hiring as we know it
is broken on many fronts.
is broken on many fronts.
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Pengkaderan dipecahkan kepada
beberapa peringkat.
beberapa peringkat.
00:14
It's a terrible experience for people.
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Ia menggerunkan ramai orang.
00:15
About 75 percent of people
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Sekitar 75 peratus pemohon yang
00:17
who applied to jobs
using various methods in the past year
using various methods in the past year
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mengguna pelbagai cara pada tahun lepas
00:20
said they never heard anything back
from the employer.
from the employer.
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mendakwa ketiadaan maklum balas majikan.
00:23
And at the company level
it's not much better.
it's not much better.
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Situasi di syarikat juga tak begitu baik.
00:25
46 percent of people get fired or quit
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46 peratus pekerja dipecat atau berhenti
00:28
within the first year
of starting their jobs.
of starting their jobs.
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dalam tempoh tahun pertama bekerja.
00:30
It's pretty mind-blowing.
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Ini amat mengejutkan.
00:31
It's also bad for the economy.
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Ia tak bagus untuk ekonomi.
00:33
For the first time in history,
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Pertama kali dalam sejarah,
00:34
we have more open jobs
than we have unemployed people,
than we have unemployed people,
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pekerjaan melebihi penggangur,
00:37
and to me that screams
that we have a problem.
that we have a problem.
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petanda wujudnya masalah.
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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Saya yakin punca utama
isu ini adalah resume.
isu ini adalah resume.
00:43
A résumé definitely has
some useful pieces in it:
some useful pieces in it:
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Resume pasti mempunyai maklumat
berguna seperti:
berguna seperti:
00:46
what roles people have had,
computer skills,
computer skills,
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jawatan terdahulu,
kemahiran komputer,
kemahiran komputer,
00:48
what languages they speak,
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penguasaan bahasa,
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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tapi ia terlepas pandang akan
potensi pemohon
potensi pemohon
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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yakni peluang penambahbaikan yang
mereka terlepas.
mereka terlepas.
00:55
And with such a quickly changing economy
where jobs are coming online
where jobs are coming online
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Kepesatan perubahan ekonomi
melahirkan kerjaya online yang
melahirkan kerjaya online yang
00:58
that might require skills that nobody has,
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perlukan skil baru
01:01
if we only look at what someone
has done in the past,
has done in the past,
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namun calon pekerja sukar didapati jika
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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pengalaman kerja yang lalu menjadi ukuran.
01:07
So this is where I think technology
can be really helpful.
can be really helpful.
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Di sinilah teknologi akan sangat membantu.
01:09
You've probably seen
that algorithms have gotten pretty good
that algorithms have gotten pretty good
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Kita sedia maklum bahawa sistem
algoritma semakin mahir
algoritma semakin mahir
01:12
at matching people to things,
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memadankan citarasa pengguna.
01:14
but what if we could use
that same technology
that same technology
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Mengapa tidak kita gunakan
teknologi yang sama
teknologi yang sama
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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untuk membantu kita mencari kerjaya
yang bersesuaian?
yang bersesuaian?
01:19
But I know what you're thinking.
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Saya tahu kerisauan anda.
01:21
Algorithms picking your next job
sounds a little bit scary,
sounds a little bit scary,
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Algoritma menentukan kerjaya anda?
Kedengaran menakutkan,
Kedengaran menakutkan,
01:24
but there is one thing that has been shown
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namun ada satu kaedah yang terbukti
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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mampu menilai bakal kejayaan
seseorang dalam pekerjaan.
seseorang dalam pekerjaan.
01:29
and that's what's called
a multimeasure test.
a multimeasure test.
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Kaedah ini dinamakan
ujian aneka ukuran
ujian aneka ukuran
01:31
Multimeasure tests
really aren't anything new,
really aren't anything new,
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Ujian ini bukanlah sesuatu yang baru
01:33
but they used to be really expensive
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tapi kosnya agak mahal. Ia perlu
01:35
and required a PhD sitting across from you
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pemilik PhD menyelia ujian anda,
01:37
and answering lots of questions
and writing reports.
and writing reports.
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menjawab banyak soalan
dan menulis laporan.
dan menulis laporan.
01:39
Multimeasure tests are a way
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Ujian aneka ukuran adalah satu cara
01:41
to understand someone's inherent traits --
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untuk memahami perwatakan, daya ingatan,
01:43
your memory, your attentiveness.
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& daya perhatian seseorang.
01:46
What if we could take multimeasure tests
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Mengapa tidak kita
memanfaatkan ujian
memanfaatkan ujian
01:48
and make them scalable and accessible,
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ini, meluaskan penggunaannya,
01:51
and provide data to employers
about really what the traits are
about really what the traits are
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dan menyediakan data kepada majikan
tentang perwatakan yang sesuai
tentang perwatakan yang sesuai
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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bagi seseorang yang bakal mengisi
jawatan itu?
jawatan itu?
01:57
This all sounds abstract.
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Semua kedengaran abstrak?
01:58
Let's try one of the games together.
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Mari kita cuba satu permainan.
02:00
You're about to see a flashing circle,
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Anda akan melihat bulatan berkelip.
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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Tugas anda, tepuk tangan tika
bulatan berwarna merah & berdiam
bulatan berwarna merah & berdiam
02:06
and do nothing when it's green.
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diri tika bulatan hijau.
02:07
[Ready?]
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[Sedia?]
02:09
[Begin!]
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[Mula!]
02:11
[Green circle]
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[Bulatan hijau]
02:13
[Green circle]
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[Bulatan hijau]
02:15
[Red circle]
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[Bulatan merah]
02:17
[Green circle]
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[Bulatan hijau]
02:19
[Red circle]
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[Bulatan merah]
02:21
Maybe you're the type of person
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Mungkin anda seorang yang menepuk
02:23
who claps the millisecond
after a red circle appears.
after a red circle appears.
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tangan selepas beberapa
detik bulatan merah muncul.
detik bulatan merah muncul.
02:26
Or maybe you're the type of person
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Mungkin juga anda seorang yang
02:27
who takes just a little bit longer
to be 100 percent sure.
to be 100 percent sure.
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mengambil sedikit masa
untuk 100 peratus yakin. Mungkin
untuk 100 peratus yakin. Mungkin
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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juga anda bertepuk tangan tika
bulatan hijau walau tak boleh.
bulatan hijau walau tak boleh.
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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Yang menariknya, ini bukan
ujian standard yang menentukan
ujian standard yang menentukan
02:36
where some people are employable
and some people aren't.
and some people aren't.
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kelayakan seseorang
untuk pekerjaan atau tidak.
untuk pekerjaan atau tidak.
02:39
Instead it's about understanding
the fit between your characteristics
the fit between your characteristics
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Sebaliknya ia mengenai pemahaman
tentang kepadananan perwatakan anda
tentang kepadananan perwatakan anda
02:42
and what would make you
good a certain job.
good a certain job.
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dan kerjaya yang bersesuaian.
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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Menurut ujian ini jika anda lambat tepuk
tika merah dan tak tepuk tika hijau,
tika merah dan tak tepuk tika hijau,
02:48
you might be high in attentiveness
and high in restraint.
and high in restraint.
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anda mungkin mempunyai
daya perhatian & kekangan yang tinggi.
daya perhatian & kekangan yang tinggi.
02:51
People in that quadrant tend to be
great students, great test-takers,
great students, great test-takers,
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Orang dalam kelompok ini bakal menjadi
pelajar dan calon ujian yang hebat,
pelajar dan calon ujian yang hebat,
02:55
great at project management or accounting.
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pakar menguruskan projek
atau perakaunan.
atau perakaunan.
02:57
But if you clap immediately on red
and sometimes clap on green,
and sometimes clap on green,
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Namun jika anda tepuk cepat tika
merah & kadang-kala tepuk tika hijau
merah & kadang-kala tepuk tika hijau
03:00
that might mean that
you're more impulsive and creative,
you're more impulsive and creative,
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kemungkinan anda
lebih mengikut gerak hati & kreatif.
lebih mengikut gerak hati & kreatif.
03:03
and we've found that top-performing
salespeople often embody these traits.
salespeople often embody these traits.
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Kami mendapati jurujual yang cemerlang
sering memiliki sifat ini.
sering memiliki sifat ini.
03:07
The way we actually use this in hiring
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Ujian ini diguna dalam pengkaderan dengan
03:09
is we have top performers in a role
go through neuroscience exercises
go through neuroscience exercises
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memberi ujian neurosains
kepada calon terhebat
kepada calon terhebat
03:12
like this one.
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seperti ini.
03:14
Then we develop an algorithm
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Kemudian kita bina algoritma
03:15
that understands what makes
those top performers unique.
those top performers unique.
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yang memahami faktor yang menjadikan
mereka unik.
mereka unik.
03:18
And then when people apply to the job,
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Kemudian tika individu memohon kerja,
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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kita dapat menapis calon yang sesuai
untuk kerja itu.
untuk kerja itu.
Mungkin anda khuatir
kemungkinan bahayanya.
kemungkinan bahayanya.
03:24
So you might be thinking
there's a danger in this.
there's a danger in this.
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03:26
The work world today
is not the most diverse
is not the most diverse
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Dunia kerjaya kini
taklah terlalu pelbagai.
taklah terlalu pelbagai.
03:28
and if we're building algorithms
based on current top performers,
based on current top performers,
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Jika algoritma dibina berdasarkan
prestasi calon terhebat,
prestasi calon terhebat,
03:32
how do we make sure
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bagaimana cara memastikan
03:33
that we're not just perpetuating
the biases that already exist?
the biases that already exist?
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bahawa kita tidak meneruskan
prasangka yang sudah wujud?
prasangka yang sudah wujud?
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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Contoh, jika kita membina algoritma
berdasarkan para CEO terhebat
berdasarkan para CEO terhebat
03:40
and use the S&P 500 as a training set,
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dan menggunakan S&P 500 sebagai set
latihan,
latihan,
03:43
you would actually find
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anda akan mendapati wujud
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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kecenderungan untuk melantik
lelaki mat saleh berbanding wanita.
lelaki mat saleh berbanding wanita.
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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Itu gambaran realiti semasa
jawatan tersebut.
jawatan tersebut.
03:51
But technology actually poses
a really interesting opportunity.
a really interesting opportunity.
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Begitupun teknologi sebenarnya
mempunyai potensi yang menarik.
mempunyai potensi yang menarik.
03:54
We can create algorithms
that are more equitable
that are more equitable
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Kita boleh cipta algoritma yang lebih
saksama
saksama
03:57
and more fair than human beings
have ever been.
have ever been.
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dan lebih adil daripada manusia.
03:59
Every algorithm that we put
into production has been pretested
into production has been pretested
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Setiap algoritma yang kami hasilkan telah
diprauji untuk kepastian bahawa
diprauji untuk kepastian bahawa
04:03
to ensure that it doesn't favor
any gender or ethnicity.
any gender or ethnicity.
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ia tak memilih kasih
terhadap mana-mana jantina atau etnik.
terhadap mana-mana jantina atau etnik.
04:06
And if there's any population
that's being overfavored,
that's being overfavored,
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Sekiranya ada kelompok yang lebih disukai,
04:08
we can actually alter the algorithm
until that's no longer true.
until that's no longer true.
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kami boleh mengubah algoritma itu
sehingga ia tak lagi benar.
sehingga ia tak lagi benar.
04:12
When we focus on the inherent
characteristics
characteristics
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Apabila kita mengutamakan perwatakan yang
sesuai untuk jawatan kosong tersebut, kita
04:14
that can make somebody
a good fit for a job,
a good fit for a job,
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04:16
we can transcend racism,
classism, sexism, ageism --
classism, sexism, ageism --
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boleh mengatasi prejudis
kaum, kasta kelas,jantina, umur --
kaum, kasta kelas,jantina, umur --
04:20
even good schoolism.
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bahkan sekolah yang bagus.
04:21
Our best technology and algorithms
shouldn't just be used
shouldn't just be used
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Teknologi & algoritma terbaik
tak sepatutnya hanya digunakan
tak sepatutnya hanya digunakan
untuk mencari filem seterusnya atau
lagu kegemaran Justin Bieber yang terbaru.
lagu kegemaran Justin Bieber yang terbaru.
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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04:28
Imagine if we could harness
the power of technology
the power of technology
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Bayangkan jika kita dapat memanfaatkan
potensi teknologi
potensi teknologi
04:31
to get real guidance
on what we should be doing
on what we should be doing
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bagi mendapatkan petunjuk untuk
tindakan yang
tindakan yang
04:33
based on who we are at a deeper level.
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bersesuaian dengan
perwatakan sebenar kita.
perwatakan sebenar kita.
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