How to make applying for jobs less painful | The Way We Work, a TED series

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2019-02-09・ 1419    137


Finding a job used to start with submitting your résumé to a million listings and never hearing back from most of them. But more and more companies are using tech-forward methods to identify candidates. If AI is the future of hiring, what does that mean for you? Technologist Priyanka Jain gives a look at this new hiring landscape. The Way We Work is a TED original video series where leaders and thinkers offer practical wisdom and insight into how we can adapt and thrive amid changing workplace conventions. (Made possible with the support of Dropbox) Visit https://go.ted.com/thewaywework for more!

Instruction

Double-click on the English captions to play the video from there.

00:00
Applying for jobs online
Translator: Rusazziana Ahmad Reviewer: Aiman Manap
00:01
is one of the worst digital experiences of our time.
00:04
And applying for jobs in person really isn't much better.
00:06
[The Way We Work]
00:11
Hiring as we know it is broken on many fronts.
Memohon kerja secara online
00:13
It's a terrible experience for people.
adalah antara pengalaman pahit, permohonan
00:15
About 75 percent of people
kerja secara berhadapan juga taklah begitu bagus.
00:17
who applied to jobs using various methods in the past year
[Cara Kita Bekerja]
00:20
said they never heard anything back from the employer.
00:22
And at the company level it's not much better.
Pengkaderan dipecahkan kepada beberapa peringkat.
00:25
46 percent of people get fired or quit
Ia menggerunkan ramai orang.
00:27
within the first year of starting their jobs.
Sekitar 75 peratus pemohon yang
mengguna pelbagai cara pada tahun lepas
00:30
It's pretty mind-blowing.
00:31
It's also bad for the economy.
00:32
For the first time in history,
mendakwa ketiadaan maklum balas majikan.
00:34
we have more open jobs than we have unemployed people,
Situasi di syarikat juga tak begitu baik.
00:37
and to me that screams that we have a problem.
46 peratus pekerja dipecat atau berhenti
00:39
I believe that at the crux of all of this is a single piece of paper: the résumé.
dalam tempoh tahun pertama bekerja.
Ini amat mengejutkan.
00:43
A résumé definitely has some useful pieces in it:
Ia tak bagus untuk ekonomi.
00:45
what roles people have had, computer skills,
Pertama kali dalam sejarah,
pekerjaan melebihi penggangur,
00:47
what languages they speak,
00:49
but what it misses is what they have the potential to do
petanda wujudnya masalah.
Saya yakin punca utama isu ini adalah resume.
00:52
that they might not have had the opportunity to do in the past.
00:55
And with such a quickly changing economy where jobs are coming online
Resume pasti mempunyai maklumat berguna seperti:
00:58
that might require skills that nobody has,
jawatan terdahulu, kemahiran komputer,
01:00
if we only look at what someone has done in the past,
penguasaan bahasa,
tapi ia terlepas pandang akan potensi pemohon
01:03
we're not going to be able to match people to the jobs of the future.
yakni peluang penambahbaikan yang mereka terlepas.
01:06
So this is where I think technology can be really helpful.
Kepesatan perubahan ekonomi melahirkan kerjaya online yang
01:09
You've probably seen that algorithms have gotten pretty good
perlukan skil baru
01:12
at matching people to things,
01:13
but what if we could use that same technology
namun calon pekerja sukar didapati jika
pengalaman kerja yang lalu menjadi ukuran.
01:16
to actually help us find jobs that we're really well-suited for?
01:19
But I know what you're thinking.
Di sinilah teknologi akan sangat membantu.
01:20
Algorithms picking your next job sounds a little bit scary,
Kita sedia maklum bahawa sistem algoritma semakin mahir
01:23
but there is one thing that has been shown
memadankan citarasa pengguna.
01:25
to be really predictive of someone's future success in a job,
Mengapa tidak kita gunakan teknologi yang sama
01:28
and that's what's called a multimeasure test.
untuk membantu kita mencari kerjaya yang bersesuaian?
01:30
Multimeasure tests really aren't anything new,
Saya tahu kerisauan anda.
01:33
but they used to be really expensive
Algoritma menentukan kerjaya anda? Kedengaran menakutkan,
01:34
and required a PhD sitting across from you
01:36
and answering lots of questions and writing reports.
namun ada satu kaedah yang terbukti
mampu menilai bakal kejayaan seseorang dalam pekerjaan.
01:39
Multimeasure tests are a way
01:41
to understand someone's inherent traits --
Kaedah ini dinamakan ujian aneka ukuran
01:43
your memory, your attentiveness.
Ujian ini bukanlah sesuatu yang baru
tapi kosnya agak mahal. Ia perlu
01:46
What if we could take multimeasure tests
pemilik PhD menyelia ujian anda,
01:48
and make them scalable and accessible,
menjawab banyak soalan dan menulis laporan.
01:50
and provide data to employers about really what the traits are
Ujian aneka ukuran adalah satu cara
untuk memahami perwatakan, daya ingatan,
01:54
of someone who can make them a good fit for a job?
& daya perhatian seseorang.
01:57
This all sounds abstract.
01:58
Let's try one of the games together.
Mengapa tidak kita memanfaatkan ujian
02:00
You're about to see a flashing circle,
ini, meluaskan penggunaannya,
02:02
and your job is going to be to clap when the circle is red
dan menyediakan data kepada majikan tentang perwatakan yang sesuai
02:05
and do nothing when it's green.
bagi seseorang yang bakal mengisi jawatan itu?
02:07
[Ready?]
02:08
[Begin!]
Semua kedengaran abstrak?
Mari kita cuba satu permainan.
02:11
[Green circle]
Anda akan melihat bulatan berkelip.
02:13
[Green circle]
Tugas anda, tepuk tangan tika bulatan berwarna merah & berdiam
02:15
[Red circle]
02:17
[Green circle]
diri tika bulatan hijau.
02:19
[Red circle]
[Sedia?]
02:21
Maybe you're the type of person
[Mula!]
02:23
who claps the millisecond after a red circle appears.
[Bulatan hijau]
02:25
Or maybe you're the type of person
[Bulatan hijau]
02:27
who takes just a little bit longer to be 100 percent sure.
[Bulatan merah]
[Bulatan hijau]
02:30
Or maybe you clap on green even though you're not supposed to.
[Bulatan merah]
02:33
The cool thing here is that this isn't like a standardized test
Mungkin anda seorang yang menepuk
tangan selepas beberapa detik bulatan merah muncul.
02:36
where some people are employable and some people aren't.
02:38
Instead it's about understanding the fit between your characteristics
Mungkin juga anda seorang yang
mengambil sedikit masa untuk 100 peratus yakin. Mungkin
02:42
and what would make you good a certain job.
juga anda bertepuk tangan tika bulatan hijau walau tak boleh.
02:44
We found that if you clap late on red and you never clap on the green,
Yang menariknya, ini bukan ujian standard yang menentukan
02:47
you might be high in attentiveness and high in restraint.
kelayakan seseorang untuk pekerjaan atau tidak.
02:51
People in that quadrant tend to be great students, great test-takers,
Sebaliknya ia mengenai pemahaman tentang kepadananan perwatakan anda
02:54
great at project management or accounting.
dan kerjaya yang bersesuaian.
02:56
But if you clap immediately on red and sometimes clap on green,
Menurut ujian ini jika anda lambat tepuk tika merah dan tak tepuk tika hijau,
03:00
that might mean that you're more impulsive and creative,
anda mungkin mempunyai daya perhatian & kekangan yang tinggi.
03:02
and we've found that top-performing salespeople often embody these traits.
Orang dalam kelompok ini bakal menjadi pelajar dan calon ujian yang hebat,
03:06
The way we actually use this in hiring
pakar menguruskan projek atau perakaunan.
03:08
is we have top performers in a role go through neuroscience exercises
Namun jika anda tepuk cepat tika merah & kadang-kala tepuk tika hijau
03:12
like this one.
kemungkinan anda lebih mengikut gerak hati & kreatif.
03:13
Then we develop an algorithm
03:15
that understands what makes those top performers unique.
Kami mendapati jurujual yang cemerlang sering memiliki sifat ini.
03:17
And then when people apply to the job,
03:19
we're able to surface the candidates who might be best suited for that job.
Ujian ini diguna dalam pengkaderan dengan
memberi ujian neurosains kepada calon terhebat
03:23
So you might be thinking there's a danger in this.
seperti ini.
03:26
The work world today is not the most diverse
Kemudian kita bina algoritma
yang memahami faktor yang menjadikan mereka unik.
03:28
and if we're building algorithms based on current top performers,
Kemudian tika individu memohon kerja,
03:31
how do we make sure
03:32
that we're not just perpetuating the biases that already exist?
kita dapat menapis calon yang sesuai untuk kerja itu.
03:35
For example, if we were building an algorithm based on top performing CEOs
Mungkin anda khuatir kemungkinan bahayanya.
Dunia kerjaya kini taklah terlalu pelbagai.
03:39
and use the S&P 500 as a training set,
Jika algoritma dibina berdasarkan prestasi calon terhebat,
03:43
you would actually find
03:44
that you're more likely to hire a white man named John than any woman.
bagaimana cara memastikan
bahawa kita tidak meneruskan prasangka yang sudah wujud?
03:48
And that's the reality of who's in those roles right now.
Contoh, jika kita membina algoritma berdasarkan para CEO terhebat
03:51
But technology actually poses a really interesting opportunity.
dan menggunakan S&P 500 sebagai set latihan,
03:54
We can create algorithms that are more equitable
anda akan mendapati wujud
03:56
and more fair than human beings have ever been.
kecenderungan untuk melantik lelaki mat saleh berbanding wanita.
03:58
Every algorithm that we put into production has been pretested
Itu gambaran realiti semasa jawatan tersebut.
04:02
to ensure that it doesn't favor any gender or ethnicity.
Begitupun teknologi sebenarnya mempunyai potensi yang menarik.
04:05
And if there's any population that's being overfavored,
Kita boleh cipta algoritma yang lebih saksama
04:08
we can actually alter the algorithm until that's no longer true.
dan lebih adil daripada manusia.
Setiap algoritma yang kami hasilkan telah diprauji untuk kepastian bahawa
04:12
When we focus on the inherent characteristics
04:14
that can make somebody a good fit for a job,
ia tak memilih kasih terhadap mana-mana jantina atau etnik.
04:16
we can transcend racism, classism, sexism, ageism --
Sekiranya ada kelompok yang lebih disukai,
04:20
even good schoolism.
kami boleh mengubah algoritma itu sehingga ia tak lagi benar.
04:21
Our best technology and algorithms shouldn't just be used
04:24
for helping us find our next movie binge or new favorite Justin Bieber song.
Apabila kita mengutamakan perwatakan yang
sesuai untuk jawatan kosong tersebut, kita
04:28
Imagine if we could harness the power of technology
boleh mengatasi prejudis kaum, kasta kelas,jantina, umur --
04:30
to get real guidance on what we should be doing
bahkan sekolah yang bagus.
04:33
based on who we are at a deeper level.
Teknologi & algoritma terbaik tak sepatutnya hanya digunakan
untuk mencari filem seterusnya atau lagu kegemaran Justin Bieber yang terbaru.
Bayangkan jika kita dapat memanfaatkan potensi teknologi
bagi mendapatkan petunjuk untuk tindakan yang
bersesuaian dengan perwatakan sebenar kita.
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