How to make applying for jobs less painful
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0:01 - 0:02Applying for jobs online
-
0:02 - 0:05is one of the worst
digital experiences of our time. -
0:05 - 0:07And applying for jobs in person
really isn't much better. -
0:07 - 0:09[The Way We Work]
-
0:12 - 0:14Hiring as we know it
is broken on many fronts. -
0:14 - 0:16It's a terrible experience for people.
-
0:16 - 0:18About 75 percent of people
-
0:18 - 0:21who applied to jobs
using various methods in the past year -
0:21 - 0:23said they never heard anything back
from the employer. -
0:23 - 0:26And at the company level
it's not much better. -
0:26 - 0:2846 percent of people get fired or quit
-
0:28 - 0:31within the first year
of starting their jobs. -
0:31 - 0:32It's pretty mind-blowing.
-
0:32 - 0:33It's also bad for the economy.
-
0:33 - 0:35For the first time in history,
-
0:35 - 0:38we have more open jobs
than we have unemployed people, -
0:38 - 0:40and to me that screams
that we have a problem. -
0:40 - 0:44I believe that at the crux of all of this
is a single piece of paper: the résumé. -
0:44 - 0:46A résumé definitely has
some useful pieces in it: -
0:46 - 0:48what roles people have had,
computer skills, -
0:48 - 0:50what languages they speak,
-
0:50 - 0:53but what it misses is
what they have the potential to do -
0:53 - 0:56that they might not have had
the opportunity to do in the past. -
0:56 - 0:59And with such a quickly changing economy
where jobs are coming online -
0:59 - 1:01that might require skills that nobody has,
-
1:01 - 1:04if we only look at what someone
has done in the past, -
1:04 - 1:07we're not going to be able
to match people to the jobs of the future. -
1:07 - 1:10So this is where I think technology
can be really helpful. -
1:10 - 1:13You've probably seen
that algorithms have gotten pretty good -
1:13 - 1:14at matching people to things,
-
1:14 - 1:17but what if we could use
that same technology -
1:17 - 1:20to actually help us find jobs
that we're really well-suited for? -
1:20 - 1:21But I know what you're thinking.
-
1:21 - 1:24Algorithms picking your next job
sounds a little bit scary, -
1:24 - 1:26but there is one thing that has been shown
-
1:26 - 1:29to be really predictive
of someone's future success in a job, -
1:29 - 1:31and that's what's called
a multimeasure test. -
1:31 - 1:33Multimeasure tests
really aren't anything new, -
1:33 - 1:35but they used to be really expensive
-
1:35 - 1:37and required a PhD sitting across from you
-
1:37 - 1:40and answering lots of questions
and writing reports. -
1:40 - 1:41Multimeasure tests are a way
-
1:41 - 1:44to understand someone's inherent traits --
-
1:44 - 1:46your memory, your attentiveness.
-
1:47 - 1:49What if we could take multimeasure tests
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1:49 - 1:51and make them scalable and accessible,
-
1:51 - 1:55and provide data to employers
about really what the traits are -
1:55 - 1:57of someone who can make
them a good fit for a job? -
1:58 - 1:59This all sounds abstract.
-
1:59 - 2:01Let's try one of the games together.
-
2:01 - 2:02You're about to see a flashing circle,
-
2:02 - 2:05and your job is going to be
to clap when the circle is red -
2:06 - 2:08and do nothing when it's green.
-
2:08 - 2:09[Ready?]
-
2:09 - 2:11[Begin!]
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2:12 - 2:13[Green circle]
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2:14 - 2:15[Green circle]
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2:16 - 2:17[Red circle]
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2:18 - 2:19[Green circle]
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2:20 - 2:21[Red circle]
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2:22 - 2:24Maybe you're the type of person
-
2:24 - 2:26who claps the millisecond
after a red circle appears. -
2:26 - 2:28Or maybe you're the type of person
-
2:28 - 2:30who takes just a little bit longer
to be 100 percent sure. -
2:31 - 2:33Or maybe you clap on green
even though you're not supposed to. -
2:33 - 2:36The cool thing here is that
this isn't like a standardized test -
2:36 - 2:39where some people are employable
and some people aren't. -
2:39 - 2:42Instead it's about understanding
the fit between your characteristics -
2:42 - 2:44and what would make you
good a certain job. -
2:44 - 2:48We found that if you clap late on red
and you never clap on the green, -
2:48 - 2:51you might be high in attentiveness
and high in restraint. -
2:51 - 2:55People in that quadrant tend to be
great students, great test-takers, -
2:55 - 2:57great at project management or accounting.
-
2:57 - 3:01But if you clap immediately on red
and sometimes clap on green, -
3:01 - 3:03that might mean that
you're more impulsive and creative, -
3:03 - 3:07and we've found that top-performing
salespeople often embody these traits. -
3:07 - 3:09The way we actually use this in hiring
-
3:09 - 3:13is we have top performers in a role
go through neuroscience exercises -
3:13 - 3:14like this one.
-
3:14 - 3:16Then we develop an algorithm
-
3:16 - 3:18that understands what makes
those top performers unique. -
3:18 - 3:20And then when people apply to the job,
-
3:20 - 3:24we're able to surface the candidates
who might be best suited for that job. -
3:24 - 3:27So you might be thinking
there's a danger in this. -
3:27 - 3:29The work world today
is not the most diverse -
3:29 - 3:32and if we're building algorithms
based on current top performers, -
3:32 - 3:33how do we make sure
-
3:33 - 3:36that we're not just perpetuating
the biases that already exist? -
3:36 - 3:40For example, if we were building
an algorithm based on top performing CEOs -
3:40 - 3:44and use the S&P 500 as a training set,
-
3:44 - 3:45you would actually find
-
3:45 - 3:49that you're more likely to hire
a white man named John than any woman. -
3:49 - 3:51And that's the reality
of who's in those roles right now. -
3:51 - 3:55But technology actually poses
a really interesting opportunity. -
3:55 - 3:57We can create algorithms
that are more equitable -
3:57 - 3:59and more fair than human beings
have ever been. -
3:59 - 4:03Every algorithm that we put
into production has been pretested -
4:03 - 4:06to ensure that it doesn't favor
any gender or ethnicity. -
4:06 - 4:09And if there's any population
that's being overfavored, -
4:09 - 4:12we can actually alter the algorithm
until that's no longer true. -
4:12 - 4:15When we focus on the inherent
characteristics -
4:15 - 4:17that can make somebody
a good fit for a job, -
4:17 - 4:20we can transcend racism,
classism, sexism, ageism -- -
4:20 - 4:22even good schoolism.
-
4:22 - 4:25Our best technology and algorithms
shouldn't just be used -
4:25 - 4:29for helping us find our next movie binge
or new favorite Justin Bieber song. -
4:29 - 4:31Imagine if we could harness
the power of technology -
4:31 - 4:34to get real guidance
on what we should be doing -
4:34 - 4:35based on who we are at a deeper level.
- Title:
- How to make applying for jobs less painful
- Speaker:
- Priyanka Jain
- Description:
-
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.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TED Series
- Duration:
- 04:49
Brian Greene approved English subtitles for How to make applying for jobs less painful | ||
Brian Greene edited English subtitles for How to make applying for jobs less painful | ||
Brian Greene accepted English subtitles for How to make applying for jobs less painful | ||
Brian Greene edited English subtitles for How to make applying for jobs less painful | ||
Brian Greene edited English subtitles for How to make applying for jobs less painful | ||
Brian Greene edited English subtitles for How to make applying for jobs less painful |