Applying for jobs online is one of the worst digital experiences of our time. And applying for jobs in person really isn't much better.
Melamar kerja secara daring adalah salah satu pengalaman digital paling meresahkan. Melamar secara langsung pun tak jauh berbeda.
[The Way We Work]
[Cara Kita Bekerja] (Musik)
Hiring as we know it is broken on many fronts. It's a terrible experience for people. About 75 percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer. And at the company level it's not much better. 46 percent of people get fired or quit within the first year of starting their jobs. It's pretty mind-blowing. It's also bad for the economy. For the first time in history, we have more open jobs than we have unemployed people, and to me that screams that we have a problem.
Ada banyak cela di berbagai sisi perekrutan. Rekrutmen pun jadi pengalaman buruk. Sekitar 75 persen orang yang melamar kerja dengan beragam metode setahun belakangan, mengaku tak pernah mendengar kabar lagi dari pemberi kerja. Perusahaan pun tak jauh berbeda. Sekitar 46% pekerja dipecat atau berhenti dalam tahun pertama bekerja. Cukup mengejutkan dan buruk bagi perekonomian. Pertama kalinya dalam sejarah, jumlah lowongan lebih banyak daripada pengangguran. Artinya, kita punya masalah. Aku yakin semua ini berawal dari selembar kertas: resume.
I believe that at the crux of all of this is a single piece of paper: the résumé. A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak, but what it misses is what they have the potential to do that they might not have had the opportunity to do in the past. And with such a quickly changing economy where jobs are coming online that might require skills that nobody has, if we only look at what someone has done in the past, we're not going to be able to match people to the jobs of the future.
Resume berisi hal-hal penting. Pengalaman kerja pelamar, kemahiran komputer, bahasa yang dikuasai. Namun, resume tak membahas potensi yang belum diperdalam oleh pelamar. Dengan perekonomian yang cepat berubah sehingga pekerjaan jadi serba daring, keahlian yang sungguh baru mungkin diperlukan. Jika kita cuma melihat keahlian yang sudah-sudah, akan sulit menyerasikan orang ke pekerjaan masa depan. Menurutku, di sinilah teknologi berguna.
So this is where I think technology can be really helpful. You've probably seen that algorithms have gotten pretty good at matching people to things, but what if we could use that same technology to actually help us find jobs that we're really well-suited for? But I know what you're thinking. Algorithms picking your next job sounds a little bit scary, but there is one thing that has been shown to be really predictive of someone's future success in a job, and that's what's called a multimeasure test.
Kau mungkin tahu kalau algoritma makin pandai mengategorikan pilihan orang. Tapi, apa jadinya bila kita gunakan teknologi ini, untuk membantu menemukan pekerjaan yang sesuai? Aku tahu apa pendapatmu. Algoritma yang mencarikan pekerjaanmu memang terdengar seram. Namun, ada satu hal yang terbukti bisa memperkirakan keberhasilan pekerjaan seseorang kelak. Itulah yang disebut tes multi-ukur. Tes multi-ukur bukanlah hal baru,
Multimeasure tests really aren't anything new, but they used to be really expensive and required a PhD sitting across from you and answering lots of questions and writing reports. Multimeasure tests are a way to understand someone's inherent traits -- your memory, your attentiveness. What if we could take multimeasure tests and make them scalable and accessible, and provide data to employers about really what the traits are of someone who can make them a good fit for a job?
tapi umumnya sangat mahal dan harus didampingi seorang doktor serta perlu menjawab banyak pertanyaan dan menulis laporan. Tes multi-ukur adalah cara memahami sifat bawaan seseorang, ingatanmu, ketelitianmu. Apa jadinya bila kita gunakan tes ini dan membuatnya terukur serta mudah diakses selagi memberi data kepada pemberi kerja tentang sifat macam apa yang menunjukkan kecocokan seseorang dengan pekerjaannya? Memang terdengar abstrak.
This all sounds abstract. Let's try one of the games together. You're about to see a flashing circle, and your job is going to be to clap when the circle is red and do nothing when it's green.
Mari kita coba satu permainan. Kau akan lihat sekelebat lingkaran. Tugasmu ialah bertepuk tangan saat lingkarannya berwarna merah dan diam saat warnanya hijau.
[Ready?]
[Siap?]
[Begin!]
[Mulai!]
[Green circle]
[Lingkaran Hijau]
[Green circle]
[Lingkaran Hijau]
[Red circle]
[Lingkaran Merah]
[Green circle]
[Lingkaran Hijau]
[Red circle]
[Lingkaran Merah]
Maybe you're the type of person who claps the millisecond after a red circle appears. Or maybe you're the type of person who takes just a little bit longer to be 100 percent sure. Or maybe you clap on green even though you're not supposed to. The cool thing here is that this isn't like a standardized test where some people are employable and some people aren't. Instead it's about understanding the fit between your characteristics and what would make you good a certain job. We found that if you clap late on red and you never clap on the green, you might be high in attentiveness and high in restraint. People in that quadrant tend to be great students, great test-takers, great at project management or accounting. But if you clap immediately on red and sometimes clap on green, that might mean that you're more impulsive and creative, and we've found that top-performing salespeople often embody these traits.
Mungkin kau tipe orang yang lekas bertepuk saat lingkaran merah muncul. Atau mungkin kau tipe yang menunggu sebentar agar sepenuhnya yakin. Atau mungkin kau tak sengaja bertepuk saat warnanya hijau. Bagian menariknya adalah ini bukan tes standar yang menentukan seseorang dapat dipekerjakan atau tidak. Tes ini membantu menyesuaikan karaktermu dengan pekerjaan mana yang cocok denganmu. Jika kau telat bertepuk pada warna merah dan tak pernah bertepuk pada warna hijau, kau mungkin punya tingkat ketelitian dan kontrol diri yang tinggi. Orang-orang di kuadran itu merupakan murid dan peserta ujian yang mahir, cakap dalam manajemen proyek atau akuntansi. Jika kau lekas bertepuk saat warna merah dan kadang saat hijau, kau mungkin orang yang impulsif serta kreatif dan kami melihat para sales terbaik mempunyai sifat ini. Kami bisa menerapkan tes ini di perekrutan
The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one. Then we develop an algorithm that understands what makes those top performers unique. And then when people apply to the job, we're able to surface the candidates who might be best suited for that job.
ialah karena pekerja terbaik di bidangnya menjalani uji ilmu saraf serupa. Lalu, kami membuat algoritma yang memahami keunikan dari para pekerja terbaik ini. Saat ada yang melamar suatu pekerjaan, kami bisa memperkirakan kandidat yang paling sesuai untuk pekerjaan itu. Kau mungkin menduga tes ini berisiko.
So you might be thinking there's a danger in this. The work world today is not the most diverse and if we're building algorithms based on current top performers, how do we make sure that we're not just perpetuating the biases that already exist? For example, if we were building an algorithm based on top performing CEOs and use the S&P 500 as a training set, you would actually find that you're more likely to hire a white man named John than any woman. And that's the reality of who's in those roles right now. But technology actually poses a really interesting opportunity. We can create algorithms that are more equitable and more fair than human beings have ever been. Every algorithm that we put into production has been pretested to ensure that it doesn't favor any gender or ethnicity. And if there's any population that's being overfavored, we can actually alter the algorithm until that's no longer true. When we focus on the inherent characteristics that can make somebody a good fit for a job, we can transcend racism, classism, sexism, ageism -- even good schoolism.
Dunia kerja saat ini tak terlalu beragam. Jika kita membangun algoritma berdasarkan pekerja terbaik saja, bagaimana memastikan kalau kita justru tak memperkeruh bias yang telah ada? Contohnya, jika algoritma dibuat berdasarkan CEO dengan kinerja terbaik dan menggunakan indeks perusahaan besar sebagai alat uji, kemungkinannya kau cenderung merekrut pria kulit putih bernama John daripada wanita. Itulah kenyataan pimpinan perusahaan yang ada saat ini. Namun, teknologi telah membuka kesempatan yang sangat menarik. Kita bisa membuat algoritma yang lebih adil dan merata daripada yang diukur manusia. Tiap algoritma yang kami ciptakan sudah dites sebelumnya agar hasilnya tak condong ke gender atau etnik tertentu. Jika ada populasi tertentu yang lebih sering terpilih, kita bisa mengubah algoritmanya hingga hal itu tak terjadi lagi. Jika kita berfokus pada karakteristik asli yang mencocokkan seseorang ke suatu pekerjaan, kita bisa mengesampingkan pembedaan ras, kelas sosial, gender, umur, dan bahkan asal sekolahnya.
Our best technology and algorithms shouldn't just be used for helping us find our next movie binge or new favorite Justin Bieber song. Imagine if we could harness the power of technology to get real guidance on what we should be doing based on who we are at a deeper level.
Teknologi dan algoritma terbaik kita jangan hanya digunakan untuk mencari rekomendasi film atau lagu Justin Bieber terbaru. Bayangkan jika kita bisa memanfaatkan kekuatan teknologi sebagai panduan atas apa yang harusnya kita lakukan sesuai karakter kita secara mendalam.